Machine-learning-based method for fiber-bending eavesdropping detection.

In this Letter, we present a scheme for detecting fiber-bending eavesdropping based on feature extraction and machine learning (ML). First, 5-dimensional features from the time-domain signal are extracted from the optical signal, and then a long short-term memory (LSTM) network is applied for eavesdropping and normal event classification. Experimental data are collected from a 60 km single-mode fiber transmission link with eavesdropping implemented by a clip-on coupler. Results show that the proposed scheme achieves a 95.83% detection accuracy. Furthermore, since the scheme focuses on the time-domain waveform of the received optical signal, additional devices and a special link design are not required.

Effects of nine typical technologies for primary autonomous vehicles on road safety in China.

The autonomous vehicle is profoundly changing the future of transportation safety. The reduction in collisions with different injury degrees and the savings of crash-related economic costs if nine autonomous vehicle technologies were promoted to be wildly available in China are evaluated. The quantitative analysis was divided into three main parts: (1) Calculate the technical effectiveness of nine autonomous vehicle technologies in collisions through a systematic literature review; (2) Apply the technical effectiveness to estimate the potential effects on avoiding collisions and saving crash-related economic costs in China if all vehicles had these technologies; and (3) Quantify the influence of current technical limitations in speed applicability, weather applicability, light applicability, and active rate on potential effects. Definitely, these technologies have different safety benefits in different countries. The framework developed and technical effectiveness calculated in this study can be applied to evaluate the safety impact of these technologies in other countries.

Quantifying the Impact of Deployments of Autonomous Vehicles and Intelligent Roads on Road Safety in China: A Country-Level Modeling Study.

Approximately 1.35 million people lose their lives due to road traffic collisions worldwide per year. However, the variation of road safety depending on the deployment of Autonomous Vehicles (AV), Intelligent Roads (IR), and Vehicle-to-Vehicle technology (V2V) is largely unknown. In this analysis, a bottom-up analytical framework was developed to evaluate the safety benefits of avoiding road injuries and reducing crash-related economic costs from the deployment of AVs, IRs, and V2Vs in China in 26 deployment scenarios from 2020 to 2050. The results indicate that compared with only deploying AVs, increasing the availability of IRs and V2V while reducing the deployment of fully AVs can achieve larger safety benefits in China. Increasing the deployment of V2V while reducing the deployment of IRs can sometimes achieve similar safety benefits. The deployment of AVs, IRs, and V2V plays different roles in achieving safety benefits. The large-scale deployment of AVs is the foundation of reducing traffic collisions; the construction of IRs would determine the upper limit of reducing traffic collisions, and the readiness of connected vehicles would influence the pace of reducing traffic collisions, which should be designed in a coordinated manner. Only six synergetic scenarios with full equipment of V2V can meet the SDG 3.6 target for reducing casualties by 50% in 2030 compared to 2020. In general, our results highlight the importance and the potential of the deployment of AVs, IRs, and V2V to reduce road fatalities and injuries. To achieve greater and faster safety benefits, the government should prioritize to the deployment of IRs and V2V. The framework developed in this study can provide practical support for decision-makers to design strategies and policies on the deployment of AVs and IRs, which can also be applied in other countries.

Security analysis of a QAM modulated quantum noise stream cipher under a correlation attack.

Quantum noise stream cipher where encrypted signals are masked by quantum noise and ASE noise provides a physical layer of security. It requires the transmitter and the receiver to share a stream cipher that is generated from a PRNG. Yet a correlation attack threatens its security due to the mathematical properties of PRNG. This paper discusses the security of QNSC system under correlation attacks. Our experiment results find that the security of the whole system depends on the cycle to refresh the seed key and the correlation between the incepted running key, original running key, and seed key. Furthermore, it is important to provide security for the QNSC system by maintaining low optical power. Besides, this new analytical method provides quantitative security analysis for a QNSC system under a correlation attack.

Recent advances and trends of trichloroethylene biodegradation: A critical review.

Trichloroethylene (TCE) is a ubiquitous chlorinated aliphatic hydrocarbon (CAH) in the environment, which is a Group 1 carcinogen with negative impacts on human health and ecosystems. Based on a series of recent advances, the environmental behavior and biodegradation process on TCE biodegradation need to be reviewed systematically. Four main biodegradation processes leading to TCE biodegradation by isolated bacteria and mixed cultures are anaerobic reductive dechlorination, anaerobic cometabolic reductive dichlorination, aerobic co-metabolism, and aerobic direct oxidation. More attention has been paid to the aerobic co-metabolism of TCE. Laboratory and field studies have demonstrated that bacterial isolates or mixed cultures containing Dehalococcoides or Dehalogenimonas can catalyze reductive dechlorination of TCE to ethene. The mechanisms, pathways, and enzymes of TCE biodegradation were reviewed, and the factors affecting the biodegradation process were discussed. Besides, the research progress on material-mediated enhanced biodegradation technologies of TCE through the combination of zero-valent iron (ZVI) or biochar with microorganisms was introduced. Furthermore, we reviewed the current research on TCE biodegradation in field applications, and finally provided the development prospects of TCE biodegradation based on the existing challenges. We hope that this review will provide guidance and specific recommendations for future studies on CAHs biodegradation in laboratory and field applications.

Exploring Weakly Labeled Images for Video Object Segmentation With Submodular Proposal Selection.

Video object segmentation (VOS) is important for various computer vision problems, and handling it with minimal human supervision is highly desired for the large-scale applications. To bring down the supervision, existing approaches largely follow a data mining perspective by assuming the availability of multiple videos sharing the same object categories. It, however, would be problematic for the tasks that consume a single video. To address this problem, this paper proposes a novel approach that explores weakly labeled images to solve video object segmentation. Given a video labeled with a target category, images labeled with the same category are collected, from which noisy object exemplars are automatically discovered. After that the proposed approach extracts a set of region proposals on various frames and efficiently matches them with massive noisy exemplars in terms of appearance and spatial context. We then jointly select the best proposals across the video by solving a novel submodular problem that combines region voting and global region matching. Finally, the localization results are leveraged as strong supervision to guide pixel-level segmentation. Extensive experiments are conducted on two challenging public databases: Youtube-Objects and DAVIS. The results suggest that the proposed approach improves over previous weakly supervised/unsupervised approaches significantly, showing a performance even comparable with the several approaches supervised by the costly manual segmentations.