Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

Mobile Internet has profoundly reshaped modern lifestyles in various aspects. Encrypted Traffic Classification (ETC) naturally plays a crucial role in managing mobile Internet, especially with the explosive growth of mobile apps using encrypted communication. Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic. None of the existing ETC methods can address all these limitations. In this paper, we propose a novel Pre-trAining Semi-Supervised ETC framework, dubbed PASS. Our key insight is to resample the original train dataset and perform contrastive pre-training without using individual app labels directly to avoid label bias issues caused by class imbalance, while obtaining a robust feature representation to differentiate overlapping homogeneous traffic by pulling positive traffic pairs closer and pushing negative pairs away. Meanwhile, PASS designs a semi-supervised optimization strategy based on pseudo-label iteration and dynamic loss weighting algorithms in order to effectively utilize massive unlabeled traffic data and alleviate manual train dataset annotation workload. PASS outperforms state-of-the-art ETC methods and generic sampling approaches on four public datasets with significant class imbalance and traffic homogeneity, remarkably pushing the F1 of Cross-Platform215 with 1.31%, ISCX-17 with 9.12%. Furthermore, we validate the generality of the contrastive pre-training and pseudo-label iteration components of PASS, which can adaptively benefit ETC methods with diverse feature extractors.Comment: Accepted by 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, 9 pages, 6 figure

RNA sequencing reveals CircRNA expression profiles in chicken embryo fibroblasts infected with velogenic Newcastle disease virus

IntroductionNewcastle disease virus (NDV) is an important avian pathogen prevalent worldwide; it has an extensive host range and seriously harms the poultry industry. Velogenic NDV strains exhibit high pathogenicity and mortality in chickens. Circular RNAs (circRNAs) are among the most abundant and conserved eukaryotic transcripts. They are part of the innate immunity and antiviral response. However, the relationship between circRNAs and NDV infection is unclear.MethodsIn this study, we used circRNA transcriptome sequencing to analyze the differences in circRNA expression profiles post velogenic NDV infection in chicken embryo fibroblasts (CEFs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to reveal significant enrichment of differentially expressed (DE) circRNAs. The circRNA- miRNA-mRNA interaction networks were further predicted. Moreover, circ-EZH2 was selected to determine its effect on NDV infection in CEFs.ResultsNDV infection altered circRNA expression profiles in CEFs, and 86 significantly DE circRNAs were identified. GO and KEGG enrichment analyses revealed significant enrichment of DE circRNAs for metabolism-related pathways, such as lysine degradation, glutaminergic synapse, and alanine, aspartic-acid, and glutamic-acid metabolism. The circRNA- miRNA-mRNA interaction networks further demonstrated that CEFs might combat NDV infection by regulating metabolism through circRNA-targeted mRNAs and miRNAs. Furthermore, we verified that circ-EZH2 overexpression and knockdown inhibited and promoted NDV replication, respectively, indicating that circRNAs are involved in NDV replication.ConclusionsThese results demonstrate that CEFs exert antiviral responses by forming circRNAs, offering new insights into the mechanisms underlying NDV-host interactions

Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5

Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading

Numerical Simulation of Solid–Liquid Interface of GaInSb Crystal Growth with Travelling Heater Method

The heat transfer and liquid phase convection during GaInSb crystal growth via the traveling heater method (with a seed) were investigated using numerical simulation to optimize the process parameters and shorten the experimental period in order to produce a high-quality crystal widely used to make various optoelectronic devices. There will be a phenomenon of “thermal impermeability” with an increase in crystal radii for the same furnace temperature profile. The maximum furnace temperature should display an increase of at least 1030 K to 1060 K in order to ensure the successful introduction of the seed with an increase of the crystal radius from 0.01 m to 0.03 m. The interface bending of the solid–liquid interface significantly increases with an increase of the crystal radius from 0.01 m to 0.02 m by about 50%, 67%, and 140%, corresponding to the maximum furnace temperatures 1030 K, 1040 K, and 1050 K, respectively. However, it decreases significantly when the maximum temperature increases from 1030 K to 1050 K, from 0.16 to 0.05, 0.2 to 0.105, and 0.24 to 0.12, corresponding to the crystal radii 0.01 m, 0.015 m, and 0.02 m, respectively. The maximum flow velocity of melt increases slightly with the furnace maximum temperature for the same radius, less than about 6%. However, it increases significantly with the increase of the radius from 0.01 m to 0.02 m, more than 68%

Sichuan Pepper Recognition in Complex Environments: A Comparison Study of Traditional Segmentation versus Deep Learning Methods

At present, picking Sichuan pepper is mainly undertaken by people, which is inefficient and presents the possibility of workers getting hurt. It is necessary to develop an intelligent robot for picking Sichuan peppers in which the key technology is accurate segmentation by means of mechanical vision. In this study, we first took images of Sichuan peppers (Hanyuan variety) in an orchard under various conditions of light intensity, cluster numbers, and image occlusion by other elements such as leaves. Under these various image conditions, we compared the ability of different technologies to segment the images, examining both traditional image segmentation methods (RGB color space, HSV color space, k-means clustering algorithm) and deep learning algorithms (U-Net convolutional network, Pyramid Scene Parsing Network, DeeplabV3+ convolutional network). After the images had been segmented, we compared the effectiveness of each algorithm at identifying Sichuan peppers in the various types of image, using the Intersection Over Union(IOU) and Mean Pixel Accuracy(MPA) indexes to measure success. The results showed that the U-Net algorithm was the most effective in the case of single front-lit clusters light without occlusion, with an IOU of 87.23% and an MPA of 95.95%. In multiple front-lit clusters without occlusion, its IOU was 76.52% and its MPA was 94.33%. Based on these results, we propose applicable segmentation methods for an intelligent Sichuan pepper-picking robot which can identify the fruit in images from various growing environments. The research showed good accuracy for the recognition and segmentation of Sichuan peppers, which suggests that this method can provide technical support for the visual recognition of a pepper-picking robot in the field

Sichuan Pepper Recognition in Complex Environments: A Comparison Study of Traditional Segmentation versus Deep Learning Methods

At present, picking Sichuan pepper is mainly undertaken by people, which is inefficient and presents the possibility of workers getting hurt. It is necessary to develop an intelligent robot for picking Sichuan peppers in which the key technology is accurate segmentation by means of mechanical vision. In this study, we first took images of Sichuan peppers (Hanyuan variety) in an orchard under various conditions of light intensity, cluster numbers, and image occlusion by other elements such as leaves. Under these various image conditions, we compared the ability of different technologies to segment the images, examining both traditional image segmentation methods (RGB color space, HSV color space, k-means clustering algorithm) and deep learning algorithms (U-Net convolutional network, Pyramid Scene Parsing Network, DeeplabV3+ convolutional network). After the images had been segmented, we compared the effectiveness of each algorithm at identifying Sichuan peppers in the various types of image, using the Intersection Over Union(IOU) and Mean Pixel Accuracy(MPA) indexes to measure success. The results showed that the U-Net algorithm was the most effective in the case of single front-lit clusters light without occlusion, with an IOU of 87.23% and an MPA of 95.95%. In multiple front-lit clusters without occlusion, its IOU was 76.52% and its MPA was 94.33%. Based on these results, we propose applicable segmentation methods for an intelligent Sichuan pepper-picking robot which can identify the fruit in images from various growing environments. The research showed good accuracy for the recognition and segmentation of Sichuan peppers, which suggests that this method can provide technical support for the visual recognition of a pepper-picking robot in the field

Does temperature-mediated reproductive success drive the direction of species displacement in two invasive species of leafminer fly?

Liriomyza sativae and L. trifolii (Diptera: Agromyzidae) are two highly invasive species of leafmining flies, which have become established as pests of horticultural crops throughout the world. In certain regions where both species have been introduced, L. sativae has displaced L. trifolii, whereas the opposite has occurred in other regions. These opposing outcomes suggest that neither species is an inherently superior competitor. The regions where these displacements have been observed (southern China, Japan and western USA) are climatically different. We determined whether temperature differentially affects the reproductive success of these species and therefore if climatic differences could affect the outcome of interspecific interactions where these species are sympatric. The results of life table parameters indicate that both species can develop successfully at all tested temperatures (20, 25, 31, 33°C). L. sativae had consistently higher fecundities at all temperatures, but L. trifolii developed to reproductive age faster. Age-stage specific survival rates were higher for L. sativae at low temperatures, but these were higher for L. trifolii at higher temperatures. We then compared the net reproductive rates (R0) for both species in pure and mixed cultures maintained at the same four constant temperatures. Both species had significantly lower net reproductive rates in mixed species cultures compared with their respective pure species cultures, indicating that both species are subject to intense interspecific competition. Net reproductive rates were significantly greater for L. sativae than for L. trifolii in mixed species groups at the lower temperatures, whereas the opposite occurred at the higher temperature. Therefore, interactions between the species are temperature dependent and small differences could shift the competitive balance between the species. These temperature mediated effects may contribute to the current ongoing displacement of L. sativae by the more recent invader L. trifolii in warm climatic areas of China

Properties, Preparation and Applications of Low Dimensional Transition Metal Dichalcogenides

Low-dimensional layered transition metal dichalcogenides (TMDs) have recently emerged as an important fundamental research material because of their unique structural, physical and chemical properties. These novel properties make these TMDs a suitable candidate in numerous potential applications. In this review, we briefly summarize the properties of low-dimensional TMDs, and then focus on the various methods used in their preparation. The use of TMDs in electronic devices, optoelectronic devices, electrocatalysts, biosystems, and hydrogen storage is also explored. The cutting-edge future development probabilities of these materials and numerous research challenges are also outlined in this review

Net reproductive rates (<i>R₀</i>) for <i>Liriomyza sativae</i> and <i>L. trifolii</i> in pure and mixed cultures maintained at four different temperatures^#.

#<p>Values for <i>R₀</i> were logarithmically transformed [ln (<i>R₀</i>)] before analysis. Back transformed means and their 95% confidence limits (in brackets) are presented.</p>##<p>For each species, means within rows followed by the same uppercase letter are not significantly different (P>0.05).</p>###<p>For each culture type, means followed by the same lower case letter are not significantly different (P>0.05).</p