Marine bacterioplankton communities have profound impact on global biogeochemical cycles and ecological balances. However, relatively little is known about the bacterioplankton communities and the ...factors shaping their spatial distribution in subtropical island. Here, the bacterioplankton communities around a typical subtropical island, Xiamen Island, were revealed by analyzing bacterial 16S rRNA gene through quantitative PCR (qPCR) and 454 pyrosequencing methods. The qPCR results indicated that the abundance of 16S rRNA gene ranged from 2.07×107 to 2.13×108copiesmL−1 in surface seawater among eight sampling sites (S1–S8) around Xiamen Island, and the nitrogen and phosphorus-rich sites (S5 and S8) were detected with higher 16S rRNA gene abundance. Pyrosequencing evidenced that a total of 267 genera of 47 classes in 26 different phyla (or candidate phyla) and some unclassified bacteria were obtained from seawater around Xiamen Island. The most dominant phylum was Proteobacteria (49.62–76.84% among sites), followed by Bacteroidetes (6.64–20.88%), Actinobacteria (2.58–9.20%), Firmicutes (0.03–13.30%), Verrucomicrobia (0.23–2.67%) and Planctomycetes (0.14–2.20%). Among eight sites, the nitrogen and phosphorus-rich sites (S5 and S8) exhibited higher proportions of Gammaproteobacteria, Epsilonproteobacteria, Firmicutes and lower proportions of Alphaproteobacteria and Planctomycetes than other sites. S5 and S8 also had more similar β-diversity, and sampling site near the estuary (S8) showed the highest bacterial diversity. Redundancy analysis (RDA) confirmed that total nitrogen and total phosphorus significantly (P<0.05 and P<0.01, respectively) influenced the bacterioplankton communities around Xiamen Island. These results will provide insights into bacterial abundance, diversity and distribution patterns, as well as their controlling factors, in subtropical marine ecosystems.
Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), ...highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private information can be restricted to the malicious teammates. In adversarial setting, obfuscation can be added to control the observability of the adversarial observer. In this paper, we attempt to generate opponent-aware privacy-preserving plans, mainly focusing on two questions: what is opponent-aware privacy-preserving planning, and, how can we generate opponent-aware privacy-preserving plans? We first define the opponent-aware privacy-preserving planning problem, where the generated plans preserve admissible privacy. Then, we demonstrate how to generate opponent-aware privacy-preserving plans. The search-based planning algorithms were restricted to public information shared among the cooperators. The observation of the adversarial observer could be purposefully controlled by exploiting decoy goals and diverse paths. Finally, we model the security patrol problem, where the UGV restricts information sharing and attempts to obfuscate the goal. The simulation experiments with privacy leakage analysis and an indoor robot demonstration show the applicability of our proposed approaches.
Several different methods of opponent modeling were introduced, leading to the problem of intention recognition in behavior modeling. Then, the process, classification, main methods, research ...prospects and practical applications of intention recognition were analyzed inductively, the latest research in related fields were summarized. Finally, some shortcomings of the current intention recognition and design methods were pointed out and some new insights for the future research were presented.
Homomorphic encryption assisted privacy preserving learning has become one powerful technology in actual scenarios and applications. In this paper, we design a novel homomorphic CNN in conjunction ...with homomorphic encryption to make prediction on encrypted sensor data. We carry out privacy preserving learning experiments with different activation functions and evaluate the accuracy on the MSTAR dataset. Experimental results show the models' effectiveness on encrypted sensor data with privacy preserving.
With the development of science and technology, the environment where the Unmanned Ground Vehicles (UGVs) patrol is complex for the cooperative and adversarial interactions. The actions performed by ...the UGVs can be observed by others which might be cooperators or adversaries. In order to fulfill the battle field patrol task, the goals of path planning for UGVs should be controlled in the mixed cooperative and adversarial environment. In this paper, we first define the goal control problem among three different roles (actor, cooperative observer, and adversarial observer), where the actor (UGV) need to reveal the real goal tied with the truthful path to the cooperative observer while hide the real goal tied with the deceptive path to the adversarial observer. Therefore, we propose one method of generating truthful or deceptive path depending on the judgement of the current observer type. Finally, indoor robot demonstration has been performed to verify the applicable of our proposed method.
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