The Great Green Wall(GGW)initiatives are among the most ambitious endeavors in addressing global ecological challenges.Currently two prominent examples have emerged across two transcontinental arid landscapes.One is C...The Great Green Wall(GGW)initiatives are among the most ambitious endeavors in addressing global ecological challenges.Currently two prominent examples have emerged across two transcontinental arid landscapes.One is China's“Three-North Shelterbelt Program”,which formally began in 1978.Spanning 13 provinces,it aims to combat desertification in the north and northwestern regions of the country,where8 major deserts and 4 sandy lands are located,by restoring forest and grass cover and establishing a protective shelterbelt system(Zhu and Song,2021).展开更多
With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effective...With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.展开更多
This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to u...This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to use the information from harm reduction programs, especially, Needle Exchange Programs (NEPs). The approach involves using respondent-driven sampling design to collect a sample of injecting drug users who appear at site of NEP in a certain period of time and to obtain retrospective self-report data on the number of friends among the IDUs and number of needles exchanged for each sampled injecting drug user. A methodology is developed to estimate the size of injecting drug users who have ever used the NEP during the fixed period of time, and which allows us to estimate the proportion of injecting drug users in using NEP. The size of the IDU population is estimated by dividing the total number of IDUs who using NEPs during the period of time by the estimated proportion of IDUs in the group. The technique holds promise for providing data needed to answer questions such as “What is the size of an IDU population in a city?” and “Is that size changing?” and better understand the dynamics of the IDU population. The methodology described here can also be used to estimate size of other hard-to-reach population by using information from harm reduction programs.展开更多
基金supported by the Science&Technology Fundamental Resources Investigation Program(Grant No.2022FY202300)the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022IRP07)。
文摘The Great Green Wall(GGW)initiatives are among the most ambitious endeavors in addressing global ecological challenges.Currently two prominent examples have emerged across two transcontinental arid landscapes.One is China's“Three-North Shelterbelt Program”,which formally began in 1978.Spanning 13 provinces,it aims to combat desertification in the north and northwestern regions of the country,where8 major deserts and 4 sandy lands are located,by restoring forest and grass cover and establishing a protective shelterbelt system(Zhu and Song,2021).
文摘With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.
文摘This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to use the information from harm reduction programs, especially, Needle Exchange Programs (NEPs). The approach involves using respondent-driven sampling design to collect a sample of injecting drug users who appear at site of NEP in a certain period of time and to obtain retrospective self-report data on the number of friends among the IDUs and number of needles exchanged for each sampled injecting drug user. A methodology is developed to estimate the size of injecting drug users who have ever used the NEP during the fixed period of time, and which allows us to estimate the proportion of injecting drug users in using NEP. The size of the IDU population is estimated by dividing the total number of IDUs who using NEPs during the period of time by the estimated proportion of IDUs in the group. The technique holds promise for providing data needed to answer questions such as “What is the size of an IDU population in a city?” and “Is that size changing?” and better understand the dynamics of the IDU population. The methodology described here can also be used to estimate size of other hard-to-reach population by using information from harm reduction programs.
