Most spectrum distribution proposals today develop their allocation algorithms that use conflict graphs to capture interference relationships. The use of conflict graphs, however, is often questioned by the wireless community because of two issues. First, building conflict graphs requires significant overhead and hence generally does not scale to outdoor networks, and second, the resulting conflict graphs do not capture accumulative interference.In this paper, we use large-scale measurement data as ground truth to understand just how severe these issues are in practice, and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. These propagation models are imperfect, and we study the impact of their errors by tracing the impact on multiple steps in the process, from calibrating propagation models to predicting signal strength and building conflict graphs. At each step, we analyze the introduction, propagation and final impact of errors, by comparing each intermediate result to its ground truth counterpart generated from measurements. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lose some spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique that addresses any remaining accumulative interference, the last missing piece in a practical spectrum distribution system using measurement-calibrated conflict graphs.

Information regarding the infection rate and genotype shifts for Japanese encephalitis virus (JEV) are important for JE vaccine application. In Jiangsu province, China, which is one of the provinces with a high prevalence of JE, JEV infection in swine and mosquitoes in certain cities has only been investigated in 2008-2009. Lianyungang City has one of the highest numbers of JE cases in Jiangsu province, and it has a high risk of JEV invasion via migrant birds. JEV infection in vectors in Lianyungang City, which has urban and rural parts, has not been investigated. In 2015-2016, we collected mosquitoes in cowsheds with ultraviolet light traps and detected JEV by reverse transcription-polymerase chain reaction (RT-PCR) method in Culex tritaeniorhynchus in Xintan village, Xuzhuang village, and Xiaogaozhuang village in Lianyungang City, China. The proportion of positive pools, which is calculated by the number of infected pools to the total number of pools tested in these villages, were 16.67%, 20.00%, and 4.17%, respectively, and the minimum infection rates, which is calculated as the ratio of the number of positive pools to the total number of mosquitoes tested, were 3.33‰, 4.00‰, and 0.83‰, respectively. Four JEV strains from positive samples were coded as LYG-1, LYG-2, LYG-3, and LYG-4, and the complete E genes were sequenced. Furthermore, the complete genome of LYG-3 was sequenced. The phylogenetic analysis indicated that all the four JEV strains belonged to genotype I-b. This is the first report of genotype I JEV strain in Jiangsu province. The high JEV infection rate in Culex tritaeniorhynchus indicated a high risk of JE reemergence in Lianyungang. The detected JEV strains may have similar antigenicity to that of SA14-14-2 according to molecular characters. These findings suggest that the vaccine can still be effective in Lianyungang.

Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses. However, crowd-sourcing systems also pose a real challenge to existing security mechanisms deployed to protect Internet services, particularly those tools that identify malicious activity by detecting activities of automated programs such as CAPTCHAs. In this work, we leverage access to two large crowdturfing sites to gather a large corpus of ground-truth data generated by crowdturfing campaigns. We compare and contrast this data with "organic" content generated by normal users to identify unique characteristics and potential signatures for use in real-time detectors. This poster describes first steps taken focused on crowdturfing campaigns targeting the Sina Weibo microblogging system. We describe our methodology, our data (over 290K campaigns, 34K worker accounts, 61 million tweets...), and some initial results.