Security enhancement for NOMA-UAV networks

Owing to its distinctive merits, non-orthogonal multiple access (NOMA) techniques have been utilized in unmanned aerial vehicle (UAV) enabled wireless base stations to provide effective coverage for terrestrial users. However, the security of NOMA-UAV systems remains a challenge due to the line-of-sight air-to-ground channels and higher transmission power of weaker users in NOMA. In this paper, we propose two schemes to guarantee the secure transmission in UAV-NOMA networks. When only one user requires secure transmission, we derive the hovering position for the UAV and the power allocation to meet rate threshold of the secure user while maximizing the sum rate of remaining users. This disrupts the eavesdropping towards the secure user effectively. When multiple users require secure transmission, we further take the advantage of beamforming via multiple antennas at the UAV to guarantee their secure transmission. Due to the non-convexity of this problem, we convert it into a convex one for an iterative solution by using the second order cone programming. Finally, simulation results are provided to show the effectiveness of the proposed scheme

Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-the-art (SOTA) baselines. The demonstration video (https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code (https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are also provided

A Method to Detect AAC Audio Forgery

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate

Land use/land cover change and driving effects of water environment system in Dunhuang Basin, northwestern China

The Dunhuang Basin, located in northwestern China, is famous for its oases and geological remains. However, some problems of the eco-environment have raised public concern in recent decades. Land use/land cover change (LUCC) has been considered essential reference for studying eco-environment across the world. In the present study, the land use/land cover was divided into natural water, salt marshes, Aeluropus littoralis, natural vegetation, barren land, and desertified land. The LUCC was analyzed using four temporal Landsat images (from around 1975, 1990, 2000, 2010, respectively) and RapidEye images in 2010. Firstly, vegetation degeneration is the most serious problem, and 926.74 km2 turned into bare land in the past 35 years. The total area of bare land increased mainly occurred during 1975–1990. The area of desertified land increased rapidly from 2000 to 2010. Secondly, wetlands have experienced extreme shrinking; some areas degenerated into salt marshes, subsequently vanished. Salt marsh areas have been continually decreasing and gradually degenerating into saline and alkaline lands and bare land. In relation to the driving forces of LUCC, according to collected data and interpretation results by remote sensing images, the surface water environment is destructive due to three reservoirs impede surface water supplementation to the soil and natural vegetation. In addition, excessive pumping of groundwater occurred in the study area. Based on the local soil profiles of vadose zones and dynamic change of groundwater level, the groundwater flow system is another key factor, which developed along with the spatial distribution of groundwater recharge, runoff, and discharge conditions. Furthermore, large-scale activities connected to the reclamation of commercial farmlands have also promoted the LUCC

Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned faster than normal knowledge, and ii) trigger patterns have a greater effect on image classification than normal class patterns. Our approach generates the images with newly learned knowledge by identifying the differences between the old and new global models, and filters trigger images by evaluating the effect of these generated images. By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign. Comprehensive experiments demonstrate that our approach can defend against almost all the existing types of backdoor attacks and outperform all the seven state-of-the-art defense methods with both IID and non-IID scenarios. Especially, our approach can successfully defend against the backdoor attack even when 80\% of the clients are malicious

Contamination status and molecular typing of Legionella pneumophila in artificial water environment in Shanghai hospitals from 2019 to 2020

BackgroundThe incidence of Legionnaires' disease is increasing globally and artificial water environment is becoming a common source of outbreaks. Molecular typing techniques can help prevent and control Legionella. ObjectiveTo understand the molecular epidemiological characteristics of Legionella pneumophila in artificial water environment of Shanghai hospitals, and provide a scientific basis for the prevention and control of Legionnaires' disease. MethodsWater samples were collected from artificial water environment in 14 hospitals from May to October each year from 2019 to 2020 in Shanghai. A total of 984 water samples were collected from 8 Grade-A tertiary hospitals and 6 non-Grade-A tertiary hospitals, including 312 samples of cooling water, 72 samples of chilled water, and 600 samples of tap water. The water samples were isolated and serotyped for Legionella pneumophila and preserved, and the positive rate of Legionella pneumophila in the samples was used as an indicator of contamination. The preserved strains were resuscitated and 81 surviving strains were obtained for pulsed field gel electrophoresis (PFGE) typing analysis. ResultsA total of 124 Legionella pneumophila positive water samples were detected, with a positive rate of 12.60%. The positive rate was higher in the Grade-A tertiary hospitals (16.54%, 87/526) than in the non-Grade-A tertiary hospitals (8.08%, 37/458) (χ2=15.91, P<0.001). The positive rate of cooling water (23.40%) was the highest among different types of water samples, and the difference was statistically significant (χ2=61.19, P<0.001). The difference in positive rate of tap water was statistically significant among different hospital departments (χ2=11.37, P<0.05). The positive rate in 2019 (15.06%) was higher than that in 2020 (9.84%) (χ2=6.23, P<0.05). From May to October, August had the highest annual average positive rate (16.46%) and October had the lowest (8.54%), but the difference in positive rates among months was not statistically significant (χ2=5.39, P=0.37). The difference in positive rate among districts was statistically significant (χ2=24.88, P<0.001). A total of 131 strains of Legionella pneumophila were isolated, with serotype 1 (80.15%, 105/131) predominating. Among the 81 surviving strains of Legionella pneumophila subjected to PFGE typing, the band-based similarity coefficients ranged from 41.30% to 100%. Among the 29 PFGE band types (S1-S29) recorded, each band type included 1-10 strains, and S28 was the dominant band type. Four clusters (I-IV) of PFGE band types were identified, accounting for 66.67% (54/81) of all strains and containing 13 band types. ConclusionLegionella pneumophila contamination is present in the artificial water environment of hospitals in Shanghai from 2019 to 2020, and the contamination in tap water deserves attention. The detected serotype of Legionella pneumophila is predominantly type 1, and PFGE typing reveals the presence of genetic polymorphism. Therefore, the monitoring and control of Legionella pneumophila in hospital artificial water environment should be strengthened

Differences in In Vitro Digestibility of Curcumin Nanoemulsions Stabilized by Whey Protein Isolate and Whey Protein Isolate-(–)-Epigallocatechin-3-gallate

In this study, the release of free fat acids (FFA) from and the digestion characteristics of curcumin nanoemulsions constructed using whey protein isolate (WPI)-(-)-epigallocatechin-3-gallate (ECGG) graft copolymers with 3% and 4% grafting degrees as emulsifiers were investigated during in vitro simulated digestion and compared with those of curcumin nanoemulsions stabilized by WPI. It was found that binding to EGCG might cause the unfolding of WPI, and the interfacial film thickness of the WPI-EGCG stabilized emulsion increased by 31.6 nm compared with that of the WPI stabilized emulsion. The WPI-EGCG complex stabilized emulsion had a smaller particle size dispersion and average particle size than the WPI stabilized emulsion and was therefore more stable and superior in promoting lipid digestion. After 120 minutes of intestinal digestion, the final release rate of FFA from the nanoemulsion stabilized with 4% WPI-EGCG was 85.13%. Also, the graft treatment improved the bioaccessibility of curcumin encapsulated in the system