A New Method on Software Reliability Prediction

As we all know, relevant data during software life cycle can be used to analyze and predict software reliability. Firstly, the major disadvantages of the current software reliability models are discussed. And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model are proposed, and the improved model is established. Lastly, simulation results show that compared with classic models, the improved model has better prediction precision, better generalization ability, and lower dependence on the number of samples, which is more applicable for software reliability prediction

MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images

In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique.Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly.Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies.By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications. However, transformer-based architectures lack global semantic information interaction and require a large-scale training dataset, making it challenging to train with small data samples. In order to solve these challenges, we present a hierarchical contextattention transformer network (MHITNet) that combines the multi-scale, transformer, and hierarchical context extraction modules in skip-connections. The multi-scale module captures deeper CT semantic information, enabling transformers to encode feature maps of tokenized picture patches from various CNN stages as input attention sequences more effectively. The hierarchical context attention module augments global data and reweights pixels to capture semantic context.Extensive trials on three datasets show that the proposed MHITNet beats current best practise

Prediction of the post-translational modifications of adipokinetic hormone receptors from solitary to eusocial bees

Adipokinetic hormone receptor (AKHR) was regarded as the crucial regulator of lipid consuming, but now has been renewed as a pluripotent neuropeptide G protein-coupled receptor. It has been identified in all sequenced bee genomes from the solitary to the eusocial. In the current study, we try to clarify the transitions of AKHR on lipid utilization and other potential functions from solitary to eusocial bees. The results showed that the AKHRs were divided into different groups based on their social complexity approximately. Nevertheless, the critical motifs and tertiary structures were highly conserved. As to the post-translational modifications, the eusocial possessed more phosphorylation residues and modification patterns, which might be due to the necessity of more diverse functions. These results suggest that AKHRs are highly conserved on both primary motifs and tertiary structures, but more flexible on posttranslational modifications so as to accommodate to more complicated eusocial life

PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the performance of the localized model. Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data. Such a global model may overlook the specific information that the clients have been sampled. In this paper, we propose a novel scheme to inject personalized prior knowledge into the global model in each client, which attempts to mitigate the introduced incomplete information problem in PFL. At the heart of our proposed approach is a framework, the PFL with Bregman Divergence (pFedBreD), decoupling the personalized prior from the local objective function regularized by Bregman divergence for greater adaptability in personalized scenarios. We also relax the mirror descent (RMD) to extract the prior explicitly to provide optional strategies. Additionally, our pFedBreD is backed up by a convergence analysis. Sufficient experiments demonstrate that our method reaches the state-of-the-art performances on 5 datasets and outperforms other methods by up to 3.5% across 8 benchmarks. Extensive analyses verify the robustness and necessity of proposed designs.Comment: Accepted by NeurIPS 202

Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things

. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a comple

Amygdala connectivity related to subsequent stress responses during the COVID-19 outbreak

Introduction: The amygdala plays an important role in stress responses and stress-related psychiatric disorders. It is possible that amygdala connectivity may be a neurobiological vulnerability marker for stress responses or stress-related psychiatric disorders and will be useful to precisely identify the vulnerable individuals before stress happens. However, little is known about the relationship between amygdala connectivity and subsequent stress responses. The current study investigated whether amygdala connectivity measured before experiencing stress is a predisposing neural feature of subsequent stress responses while individuals face an emergent and unexpected event like the COVID-19 outbreak. Methods: Data collected before the COVID-19 pandemic from an established fMRI cohort who lived in the pandemic center in China (Hubei) during the COVID-19 outbreak were used to investigate the relationship between amygdala connectivity and stress responses during and after the pandemic in 2020. The amygdala connectivity was measured with resting-state functional connectivity (rsFC) and effective connectivity. Results: We found the rsFC of the right amygdala with the dorsomedial prefrontal cortex (dmPFC) was negatively correlated with the stress responses at the first survey during the COVID-19 outbreak, and the rsFC between the right amygdala and bilateral superior frontal gyri (partially overlapped with the dmPFC) was correlated with SBSC at the second survey. Dynamic causal modeling suggested that the self-connection of the right amygdala was negatively correlated with stress responses during the pandemic. Discussion: Our findings expand our understanding about the role of amygdala in stress responses and stress-related psychiatric disorders and suggest that amygdala connectivity is a predisposing neural feature of subsequent stress responses

Single Image Super-Resolution Using Multi-Scale Deep Encoder-Decoder with Phase Congruency Edge Map Guidance

This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder (deconvolution) network for single image super-resolution (SISR) guided by phase congruency (PC) edge map. Our system starts by a single scale symmetrical encoder-decoder structure for SISR, which is extended to a multi-scale model by integrating wavelet multi-resolution analysis into our network. The new multi-scale deep learning system allows the low resolution (LR) input and its PC edge map to be combined so as to precisely predict the multi-scale super-resolved edge details with the guidance of the high-resolution (HR) PC edge map. In this way, the proposed deep model takes both the reconstruction of image pixels’ intensities and the recovery of multi-scale edge details into consideration under the same framework. We evaluate the proposed model on benchmark datasets of different data scenarios, such as Set14 and BSD100 - natural images, Middlebury and New Tsukuba - depth images. The evaluations based on both PSNR and visual perception reveal that the proposed model is superior to the state-of-the-art methods

Paeonol Oxime Inhibits bFGF-Induced Angiogenesis and Reduces VEGF Levels in Fibrosarcoma Cells

Background: We previously reported the anti-angiogenic activity of paeonol isolated from Moutan Cortex. In the present study, we investigated the negative effect of paeonol oxime (PO, a paeonol derivative) on basic fibroblast growth factor (bFGF)-mediated angiogenesis in human umbilical vein endothelial cells (HUVECs) (including tumor angiogenesis) and pro-survival activity in HT-1080 fibrosarcoma cell line. Methodology/Principal Findings: We showed that PO (IC50  = 17.3 µg/ml) significantly inhibited bFGF-induced cell proliferation, which was achieved with higher concentrations of paeonol (IC50 over 200 µg). The treatment with PO blocked bFGF-stimulated migration and in vitro capillary differentiation (tube formation) in a dose-dependent manner. Furthermore, PO was able to disrupt neovascularization in vivo. Interestingly, PO (25 µg/ml) decreased the cell viability of HT-1080 fibrosarcoma cells but not that of HUVECs. The treatment with PO at 12.5 µg/ml reduced the levels of phosphorylated AKT and VEGF expression (intracellular and extracelluar) in HT-1080 cells. Consistently, immunefluorescence imaging analysis revealed that PO treatment attenuated AKT phosphorylation in HT-1080 cells. Conclusions/Significance: Taken together, these results suggest that PO inhibits bFGF-induced angiogenesis in HUVECs and decreased the levels of PI3K, phospho-AKT and VEGF in HT-1080 cells