The success of online social networks has attracted a constant interest in attacking and exploiting them. Attackers usually control malicious accounts, including both fake and compromised real user accounts, to launch attack campaigns such as social spam, malware distribution, and online rating distortion.To defend against these attacks, we design and implement a malicious account detection system called SynchroTrap. We observe that malicious accounts usually perform loosely synchronized actions in a variety of social network context. Our system clusters user accounts according to the similarity of their actions and uncovers large groups of malicious accounts that act similarly at around the same time for a sustained period of time. We implement SynchroTrap as an incremental processing system on Hadoop and Giraph so that it can process the massive user activity data in a large online social network efficiently. We have deployed our system in five applications at Facebook and Instagram. SynchroTrap was able to unveil more than two million malicious accounts and 1156 large attack campaigns within one month.

Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer's conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 ¼ 0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment (R 2 ¼ 0.86). The transferability to the farmer's field was supported with a strong correlation between simulated and measured values (R 2 ¼ 0.90). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Background Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF). Results Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy ( R 2 = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics. Conclusions Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.

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