An Icn-Based Secure Task Cooperation Scheme in Challenging Wireless Edge Networks

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking (ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, the specific naming convention is designed to support task cooperation and the acquisition of keys. In addition, our scheme implements fine-grained access control for data reuse in task cooperation combined with attribute-based encryption. Experimental results show that our scheme enhances the security of task cooperation with low cost compared with existing schemes

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced

Molecular Beam Epitaxy Growth of Superconducting LiFeAs Film on SrTiO3(001) Substrate

The stoichiometric "111" iron-based superconductor, LiFeAs, has attacted great research interest in recent years. For the first time, we have successfully grown LiFeAs thin film by molecular beam epitaxy (MBE) on SrTiO3(001) substrate, and studied the interfacial growth behavior by reflection high energy electron diffraction (RHEED) and low-temperature scanning tunneling microscope (LT-STM). The effects of substrate temperature and Li/Fe flux ratio were investigated. Uniform LiFeAs film as thin as 3 quintuple-layer (QL) is formed. Superconducting gap appears in LiFeAs films thicker than 4 QL at 4.7 K. When the film is thicker than 13 QL, the superconducting gap determined by the distance between coherence peaks is about 7 meV, close to the value of bulk material. The ex situ transport measurement of thick LiFeAs film shows a sharp superconducting transition around 16 K. The upper critical field, Hc2(0)=13.0 T, is estimated from the temperature dependent magnetoresistance. The precise thickness and quality control of LiFeAs film paves the road of growing similar ultrathin iron arsenide films.Comment: 7 pages, 6 figure

Fibrinogen deposition on silicone oil-infused silver-releasing urinary catheters compromises antibiofilm and anti-encrustation properties

[Image: see text] Slippery silicone-oil-infused (SOI) surfaces have recently emerged as a promising alternative to conventional anti-infection coatings for urinary catheters to combat biofilm and encrustation formation. Benefiting from the ultralow low hysteresis and slippery behavior, the liquid-like SOI coatings have been found to effectively reduce bacterial adhesion under both static and flow conditions. However, in real clinical settings, the use of catheters may also trigger local inflammation, leading to release of host-secreted proteins, such as fibrinogen (Fgn) that deposits on the catheter surfaces, creating a niche that can be exploited by uropathogens to cause infections. In this work, we report on the fabrication of a silicone oil-infused silver-releasing catheter which exhibited superior durability and robust antibacterial activity in aqueous conditions, reducing biofilm formation of two key uropathogens Escherichia coli and Proteus mirabilis by ∼99%, when compared with commercial all-silicone catheters after 7 days while remaining noncytotoxic toward L929 mouse fibroblasts. After exposure to Fgn, the oil-infused surfaces induced conformational changes in the protein which accelerated adsorption onto the surfaces. The deposited Fgn blocked the interaction of silver with the bacteria and served as a scaffold, which promoted bacterial colonization, resulting in a compromised antibiofilm activity. Fgn binding also facilitated the migration of Proteus mirabilis over the catheter surfaces and accelerated the deposition and spread of crystalline biofilm. Our findings suggest that the use of silicone oil-infused silver-releasing urinary catheters may not be a feasible strategy to combat infections and associated complications arising from severe inflammation

A Bridge Vibration Measurement Method by UAVs based on CNNs ‎and Bayesian Optimization‎

A bridge vibration measurement method by Unmanned Aerial Vehicles (UAVs) based on a Convolutional Neural Network (CNN) and Bayesian Optimization (BO) is proposed. In the proposed method, the video of the bridge structure is collected by a UAV, then the reference points in the background of the bridge and the target points on the bridge in the video are tracked by the Kanade-Lucas-Tomasi (KLT) optical flow method, so that their coordinates can be obtained. The BO is used to find the optimal hyper-parameter combination of a CNN, and the CNN based on BO is used to correct the bridge displacement signal collected by the UAV. Finally, the natural frequency of the bridge is extracted by processing the corrected displacement signals with Operational Modal Analysis (OMA). Moreover, a steel truss is used as the experimental model. The number of reference points and the shooting time of the UAV with the optimal correction effect of the BO-based CNN are obtained by two groups of comparative experiments, and the influence of the distance between structure and reference points on the correction effect of the BO-based CNN is determined by another group of comparative experiment. The static reference points are not required for the proposed method, which evidently enhances the applicability of UAVs; the conclusion of this paper has great guiding significance for the actual bridge vibration measurement