As a route to accessing the potential chemical diversity of uncultivable microbes from the soil, combinatorial biosynthetic libraries were constructed by cloning large fragments of DNA isolated from soil into a Streptomyces lividans host. Four novel compounds, terragines A (1), B (2), C (3), and D (4), were isolated from recombinant 436-s4-5b1, and another novel compound, terragine E (5), was isolated from 446-s3-102g1. The structures were determined by a combination of spectroscopic techniques, primarily 2D NMR.
Video streaming has become one of the most prevalent mobile applications and uses a substantial portion of the traffic on mobile networks today. With the limited bandwidth of mobile networks, understanding the user perception of the quality (i.e., Quality of Experience or QoE) of video streaming services is thus paramount for content providers and content-delivery network providers to flexibly configure network bandwidth, video servers, routing devices, and other network resources to save energy in smart cities. Although various video QoE assessment approaches have been proposed using different key performance indicators (KPIs), they all essentially relate to a common parameter: bitrate. However, because YouTube has adopted hyper text transfer protocol over secure socket layer (HTTPS) as its adaptive video streaming method to better protect user privacy and network security, bitrate can no longer be obtained from encrypted video traffic via typical deep packet inspection. In this paper, we address this challenge by proposing a machine-learning-based bitrate estimation (MBE) approach to parse bitrate information from IP packet level measurements. First, we filter HTTPS YouTube traffic based on the previously established video server IP according to the data packet googlevideo field. Then, we identify the transmission mode according to the traffic characteristics of several previous packets. Next, we identify the bitrates and resolutions of HTTP Live Streaming and Dynamic Adaptive Streaming over HTTP modes according to the characteristics of video chunks. Finally, for evaluating the effectiveness of MBE, we have chosen the video Mean Opinion Score (vMOS) proposed by a leading telecom vendor as the QoE assessment framework, and have conducted comprehensive experiments to study the impact of bitrate estimation accuracy on its KPIs for the HTTPS YouTube video streaming service. Experimental results show that MBE is a feasible and highly effective QoE evaluation approach to flexibly configure network resources in smart cities. INDEX TERMSHyper text transfer protocol over secure socket layer (HTTPS) YouTube, QoE assessment, adaptive streaming, machine learning, smart city.
In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-means) based on MapReduce according to characteristics of large meteorological datasets. The experimental results show that MK-means has more computing ability and scalability.
With widely adoption of online services, malicious web sites have become a malignant tumor of the Internet. Through system vulnerabilities, attackers can upload malicious files (which are also called webshells) to web server to create a backdoor for hackers' further attacks. Therefore, finding and detecting webshell inside web application source code are crucial to secure websites. In this paper, we propose a novel method based on the optimal threshold values to identify files that contain malicious codes from web applications. Our detection system will scan and look for malicious codes inside each file of the web application, and automatically give a list of suspicious files and a detail log analysis table of each suspicious file for administrators to check further. The Experimental results show that our approach is applicable to identify webshell more efficient than some other approaches.
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