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Institution

Chinese Ministry of Public Security

GovernmentBeijing, China
About: Chinese Ministry of Public Security is a government organization based out in Beijing, China. It is known for research contribution in the topics: Population & Cloud computing. The organization has 2655 authors who have published 2046 publications receiving 16739 citations.


Papers
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Journal ArticleDOI
TL;DR: Security solutions are presented for securing vehicular interactions in EVCE computing, and Blockchain-inspired data coins and energy coins are proposed based on distributed consensus to achieve the proof of work.
Abstract: EVCE computing is an attractive network paradigm involving seamless connections among heterogeneous vehicular contexts. It will be a trend along with EVs becoming popular in V2X. The EVs act as potential resource infrastructures referring to both information and energy interactions, and there are serious security challenges for such hybrid cloud and edge computing. Context-aware vehicular applications are identified according to the perspectives of information and energy interactions. Blockchain-inspired data coins and energy coins are proposed based on distributed consensus, in which data contribution frequency and energy contribution amount are applied to achieve the proof of work. Security solutions are presented for securing vehicular interactions in EVCE computing.

323 citations

Journal ArticleDOI
TL;DR: This paper design a storage scheme to manage personal medical data based on blockchain and cloud storage and a service framework for sharing medical records is described.
Abstract: Accurate and complete medical data are one valuable asset for patients. Privacy protection and the secure storage of medical data are crucial issues during medical services. Secure storage and making full use of personal medical records has always been a concern for the general population. The emergence of blockchain technology brings a new idea to solve this problem. As a hash chain with the characteristics of decentralization, verifiability and immutability, blockchain technology can be used to securely store personal medical data. In this paper, we design a storage scheme to manage personal medical data based on blockchain and cloud storage. Furthermore, a service framework for sharing medical records is described. In addition, the characteristics of the medical blockchain are presented and analyzed through a comparison with traditional systems. The proposed storage and sharing scheme does not depend on any third-party and no single party has absolute power to affect the processing.

286 citations

Journal ArticleDOI
TL;DR: This study draws together humans and dogs in their recent genomic evolution, for the first time, and identifies a list of genes under positive selection during domestication, which overlaps extensively with the corresponding list of positively selected genes in humans.
Abstract: The genetic bases of demographic changes and artificial selection underlying domestication are of great interest in evolutionary biology. Here we perform whole-genome sequencing of multiple grey wolves, Chinese indigenous dogs and dogs of diverse breeds. Demographic analysis show that the split between wolves and Chinese indigenous dogs occurred 32,000 years ago and that the subsequent bottlenecks were mild. Therefore, dogs may have been under human selection over a much longer time than previously concluded, based on molecular data, perhaps by initially scavenging with humans. Population genetic analysis identifies a list of genes under positive selection during domestication, which overlaps extensively with the corresponding list of positively selected genes in humans. Parallel evolution is most apparent in genes for digestion and metabolism, neurological process and cancer. Our study, for the first time, draws together humans and dogs in their recent genomic evolution.

267 citations

Journal ArticleDOI
TL;DR: An ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection that can effectively reduce the number of features and has a high detection rate and classification accuracy when compared to other classification techniques.
Abstract: Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

255 citations

Journal ArticleDOI
TL;DR: Evidence from chemical, mineralogical, biological, and packaging analysis suggested that at least some of the counterfeits were manufactured in southeast People's Republic of China, and this prompted a criminal investigation.
Abstract: Background Since 1998 the serious public health problem in South East Asia of counterfeit artesunate, containing no or subtherapeutic amounts of the active antimalarial ingredient, has led to deaths from untreated malaria, reduced confidence in this vital drug, large economic losses for the legitimate manufacturers, and concerns that artemisinin resistance might be engendered.

210 citations


Authors

Showing all 2663 results

NameH-indexPapersCitations
Yang Liu82169533657
Jinhua Sun5032810487
Lei Wu492067997
Qingsong Wang402157151
Yunhuai Liu371253799
Tingting Zhang341483537
Yunpeng Wang311444499
Saihua Jiang29852699
Weixiao Meng292483104
Xiaolei Ma29835977
Zheng Xu271212684
Huahua Xiao24551613
Peng Zeng242392339
Meng Shen221071979
Guangquan Lu21881322
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20232
202217
2021191
2020225
2019225
2018218