Virtual Private Network (VPN) can bypass censorship and access services that are geographically locked. Therefore, VPN traffic identification has become an urgent problem in traffic classification. The existing VPN traffic identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes many resources, limiting the real-time processing of traffic identification. The management of high-speed networks is mainly based on sampled traffic. As VPN traffic accounts for a relatively low proportion, it is particularly challenging to identify VPN traffic from sampled data. This paper proposes a real-time identification method for VPN traffic from sampled data in high-speed networks. In our method, we construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). To validate the usability of our method, we use 900 seconds of traffic generated on a 10 Gbps link as background traffic, mixed with V2Ray traffic, which is increasingly common in VPN traffic. With the VPN traffic proportion of 0.03%, it takes only 50.79 seconds to complete the processing at the sampling ratio of 1/256. This time is significantly less than the traffic generation time. For the effective flows extracted from sampled backbone traffic, the identification results are maintained at a high level with 97% precision, 93% recall, and 95% F1 score. In addition, our method can achieve fine-grained VPN traffic identification of different V2Ray tools.
We demonstrate an optically-driven artificial synapse based on a graphene hybrid phototransistor. Both optical memory function (long-term plasticity) and logic operations are achieved, which adds important new capabilities to photonics enabled neuromorphic computing.
Person re-identification(Person Re-ID) means that images of a pedestrian from cameras in a surveillance camera network can be automatically retrieved based on one of this pedestrian's image from another camera. The appearance change of pedestrians under different cameras poses a huge challenge to person re-identification. Person re-identification systems based on deep learning can effectively extract the appearance features of pedestrians. In this paper, the feature enhancement experiment is conducted, and the result showed that the current person reidentification datasets are relatively small and cannot fully meet the need of deep training. Therefore, this paper studied the method of using generative adversarial network to extend the person re-identification datasets and proposed a label smoothing regularization for outliers with weight (LSROW) algorithm to make full use of the generated data, effectively improved the accuracy of person re-identification.
The change in intensity, scope and component of radon released from faults can be used to analyze and evaluate the relative activity of active fault. The basic feature of main active faults in Tianshui area is analyzed. And the radon content is surveyed, the result shows that the activity of west Qinling fault is strong to extremely strong,the activity of Lixian-luojiabu fault is strong,the activity of Lixian-jishixia fault is strong to middle,the activity of west Qingshui fault is middle. The survey results accord with its activity intensity, so radon surveying can be regarded as a method for fault activity evaluation.
Our paper focuses on automating the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists. Unlike existing medical re-port generation efforts that tend to produce human-readable reports, we aim to generate medical reports that are both fluent and clinically accurate. This is achieved by our fully differentiable and end-to-end paradigm containing three complementary modules: taking the chest X-ray images and clinical his-tory document of patients as inputs, our classification module produces an internal check-list of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, giving rise to the medical reports; meanwhile, our generator also pro-duces the weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to disease-related topics.Our approach achieved promising results on commonly-used metrics concerning language fluency and clinical accuracy. Moreover, noticeable performance gains are consistently ob-served when additional input information is available, such as the clinical document and extra scans of different views.
Autonomous quantum memories are a way to passively protect quantum information using engineered dissipation that creates an "always-on'' decoder. We analyze Markovian autonomous decoders that can be implemented with a wide range of qubit and bosonic error-correcting codes, and derive several upper bounds and a lower bound on the logical error rate in terms of correction and noise rates. For many-body quantum codes, we show that, to achieve error suppression comparable to active error correction, autonomous decoders generally require correction rates that grow with code size. For codes with a threshold, we show that it is possible to achieve faster-than-polynomial decay of the logical error rate with code size by using superlogarithmic scaling of the correction rate. We illustrate our results with several examples. One example is an exactly solvable global dissipative toric code model that can achieve an effective logical error rate that decreases exponentially with the linear lattice size, provided that the recovery rate grows proportionally with the linear lattice size.
Bayesian networks is a valid probability forecast method,it is tried to use in flight data analysis and warning.Netica software packages were used in constructing Bayesian networks of actual flight data and their relationships.Especially aimed at flight departure delay.It analyzed delay reasons and delay warning by using partition time data of an airline company departure from an airfield in China.
Chinese word reduplication is the common problem when students learn Chinese.From the perspective of grammar,pragmatics,the detailed analysis of this special language phenomenon,helps to understand Russian students to learn Chinese well and the meanings in different language environments.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
