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In this study, wild barley (Hordeum brevisubulatum) infected (E+) and uninfected (E-) by Epichloë bromicola were used for hydroponic experiments during the seedling stage. Various attributes, such as the effect of fungal endophyte on the growth and development of wild barley, the absorption of cadmium (Cd) and mineral elements (Ca, Mg, Fe, Mn, Cu, Zn), subcellular distribution, and chemical forms were investigated under CdCl2 stress. The results showed that the fungal endophy significantly reduced the Ca content and percentage of plant roots under Cd stress. The Fe and Mn content of roots, the mineral element content of soluble fractions, and the stems in the pectin acid or protein-chelated state increased significantly in response to fungal endophy. Epichloë endophyte helped Cd2+ to enter into plants; and reduced the positive correlation of Ca-Fe and Ca-Mn in roots. In addition, it also decreased the correlation of soluble components Cd-Cu, Cd-Ca, Cd-Mg in roots, and the negative correlation between pectin acid or protein-chelated Cd in stems and mineral elements, to increase the absorbance of host for mineral elements. In conclusion, fungal endophy regulated the concentration and distribution of mineral elements, while storing more Cd2+ to resist the damage caused by Cd stress. The study could provide a ground for revealing the Cd tolerance mechanism of endophytic fungal symbionts. The present study is the first to study the effect of fungal endophy on essential mineral elements of plants under heavy metal stress, filling a gap in the existing research. The study could be helpful to reveal the mechanism of endophytic fungi to improve the host's tolerance to heavy metals and provide a foundation for the grass-endophyte symbionts to improve heavy metal-contaminated soils as ecological grasses. [ABSTRACT FROM AUTHOR]

In this paper, we construct a deep framework for full-reference image quality assessment (FR-IQA) by combining convolution and self-attention features; this approach effectively uses multiscale features to mimic the image evaluation in human eyes. We achieve the integration of information from local to global. First, convolutional neural network (CNN) and encoder of Swin transformer are used to extract multiscale paired features. Second, for each type of features, we fuse them after converting them to a fixed number of channels. For the fused features, we use the square of the difference between two pixel values at the corresponding channel positions to represent the features of the distortion degree. Then, we introduce a recurrent neural network (RNN) to capture the global features. Finally, for the two types of features, we use the full connection (FC) layer to regress two scores and then add the weights to compute the ultimate perceived score. By training and testing on publicly available FR-IQA datasets, experimental results further validate the superiority of our approach. [ABSTRACT FROM AUTHOR]

Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive resources, as these resources may largely remain unused in edge devices. Furthermore, current methods are mainly concentrated on improving the accuracy of DDoS attack detection and saving CPU resources, neglecting the effective allocation of computational power for benign tasks under DDoS attacks. To address these issues, this paper introduces SecEG, a secure and efficient strategy against DDoS attacks for MEC that integrates container-based task isolation with lightweight online anomaly detection on edge nodes. More specifically, a new model is proposed to analyze resource contention dynamics between DDoS attacks and benign tasks. Subsequently, by employing periodic packet sampling and real-time attack intensity predicting, an autoencoder-based method is proposed to detect DDoS attacks. We leverage an efficient scheduling method to optimize the edge resource allocation and the service quality for benign users during DDoS attacks. When executed in the real-world edge environment, our experimental findings validate the efficacy of the proposed SecEG strategy. Compared to conventional methods, the service rate of benign requests increases by 23% under intense DDoS attacks, and the CPU resource is saved up to 35%. [ABSTRACT FROM AUTHOR]

In this paper, we propose an end-to-end dual-stream transformer with a parallel encoder (DST-PE) for video captioning, which combines multimodal features and global–local representations to generate coherent captions. First, we design a parallel encoder that includes a local visual encoder and a bridge module, which simultaneously generates refined local and global visual features. Second, we devise a multimodal encoder to enhance the representation ability of our model. Finally, we adopt a transformer decoder with multimodal features as inputs and local visual features fused with textual features using a cross-attention block. Extensive experimental results demonstrate that our model achieves state-of-the-art performance with low training costs on several widely used datasets. [ABSTRACT FROM AUTHOR]

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination. [ABSTRACT FROM AUTHOR]

With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness. [ABSTRACT FROM AUTHOR]

Deep learning-based video coding methods have demonstrated superior performance compared to classical video coding standards in recent years. The vast majority of the existing deep video coding (DVC) networks are based on convolutional neural networks (CNNs), and their main drawback is that since CNNs are affected by the size of the receptive field, they cannot effectively handle long-range dependencies and local detail recovery. Therefore, how to better capture and process the overall structure as well as local texture information in the video coding task is the core issue. Notably, the transformer employs a self-attention mechanism that captures dependencies between any two positions in the input sequence without being constrained by distance limitations. This is an effective solution to the problem described above. In this paper, we propose end-to-end transformer-based adaptive video coding (TAVC). First, we compress the motion vector and residuals through a compression network built on the vision transformer (ViT) and design the motion compensation network based on ViT. Second, based on the requirement of video coding to adapt to different resolution inputs, we introduce a position encoding generator (PEG) as adaptive position encoding (APE) to maintain its translation invariance across different resolution video coding tasks. The experiment shows that for multiscale structural similarity index measurement (MS-SSIM) metrics, this method exhibits significant performance gaps compared to conventional engineering codecs, such as × 2 6 4 , × 2 6 5 , and VTM-15.2. We also achieved a good performance improvement compared to the CNN-based DVC methods. In the case of peak signal-to-noise ratio (PSNR) evaluation metrics, TAVC also achieves good performance. [ABSTRACT FROM AUTHOR]

Most of the existing static analysis-based detection methods adopt one or few types of typical static features for avoiding the problem of dimensionality and computational resource consumption. In order to further improve detecting accuracy with reasonable resource consumption, in this paper, a new Android malware detection model based on multiple features with feature selection method and feature vectorization method are proposed. Feature selection method for each type of features reduces the dimensionality of feature set. Weight-based feature vectorization method for API calls, intent and permission is designed to construct feature vector. Co-occurrence matrix-based vectorization method is proposed to vectorize opcode sequence. To demonstrate the effectiveness of our method, we conducted comprehensive experiments with a total of 30,000 samples. Experimental results show that our method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Custom computing architectures on field programmable gate array (FPGA) platforms are a viable solution to further accelerate convolutional neural network (CNN) inference. However, due to the large size feature map matrix data, the optimization of CNN feature maps storage computing on FPGA remains a challenge. To overcome these challenges, a FPGA-oriented memory access optimization method for CNN is proposed. Firstly, the feature map partition strategy is used to group the feature map efficiently. Second, the input and the output caching rotation methods are employed in adaptive memory access mode. Third, a caching hybrid rotation method is proposed to optimize memory access performance and can effectively reduce the access time of the CNN feature map. Experimental results based on SkyNet and VGG16 show that the inference speed of the proposed model is accelerated by 7.1 times compared with the previous conventional memory access optimization for CNN on FPGA. Through the evaluation of computational energy efficiency, our method can be improved by 6.4 times compared to the current typical accelerators. [ABSTRACT FROM AUTHOR]