An Improved ResNet-50 for Garbage Image Classification

In order to solve the classification model\u27s shortcomings, this study suggests a new trash classification model that is generated by altering the structure of the ResNet-50 network. The improvement is divided into two sections. The first section is to change the residual block. To filter the input features, the attention module is inserted into the residual block. Simultaneously, the downsampling process in the residual block is changed to decrease information loss. The second section is multi-scale feature fusion. To optimize feature usage, horizontal and vertical multi-scale feature fusion is integrated to the primary network structure. Because of the filtering and reuse of image features, the enhanced model can achieve higher classification performance than existing models for small data sets with few samples. The experimental results show that the modified model outperforms the original ResNet-50 model on the TrashNet dataset by 7.62% and is more robust. In the meanwhile, our model is more accurate than other advanced methods

Image Completion Based on Edge Prediction and Improved Generator

The existing image completion algorithms may result in problems of poor completion in the missing parts, excessive smoothing or chaotic structure of the completed areas, and large training cycle when processing more complex images. Therefore, a two-stage adversarial image completion model based on edge prediction and improved generator structure has been put forward to solve the existing problems. Firstly, Canny edge detection is utilized to extract the damaged edge image, to predict and to complete the edge information of the missing area of the image in the edge prediction network. Secondly, the predicted edge image is taken as a priori information by the Image completion network to complete the damaged area of the image, so as to make the structure information of the completed area more accurate. A-JPU module is designed to ensure the completion result and speed up training for existing models due to the enormous number of computations caused by the large use of extended convolution in the self-coding structure. Finally, the experimental results on Places 2 dataset show that the PSNR and SSIM of the image using the image completion model are higher and the subjective visual effect is closer to the real image than some other image completion models

Defending Against Local Adversarial Attacks through Empirical Gradient Optimization

Deep neural networks (DNNs) are susceptible to adversarial attacks, including the recently introduced locally visible adversarial patch attack, which achieves a success rate exceeding 96%. These attacks pose significant challenges to DNN security. Various defense methods, such as adversarial training, robust attention modules, watermarking, and gradient smoothing, have been proposed to enhance empirical robustness against patch attacks. However, these methods often have limitations concerning patch location requirements, randomness, and their impact on recognition accuracy for clean images.To address these challenges, we propose a novel defense algorithm called Local Adversarial Attack Empirical Defense using Gradient Optimization (LAAGO). The algorithm incorporates a low-pass filter before noise suppression to effectively mitigate the interference of high-frequency noise on the classifier while preserving the low-frequency areas of the images. Additionally, it emphasizes the original target features by enhancing the image gradients. Extensive experimental results demonstrate that the proposed method improves defense performance by 3.69% for 80 × 80 noise patches (representing approximately 4% of the images), while experiencing only a negligible 0.3% accuracy drop on clean images. The LAAGO algorithm provides a robust defense mechanism against local adversarial attacks, overcoming the limitations of previous methods. Our approach leverages gradient optimization, noise suppression, and feature enhancement, resulting in significant improvements in defense performance while maintaining high accuracy for clean images. This work contributes to the advancement of defense strategies against emerging adversarial attacks, thereby enhancing the security and reliability of deep neural networks

Probability based scheduling to optimize sewer maintenance

With municipal administrations and EPA (Environment Protection Agency) concentrating more on the issue of SSOs (Sanitary Sewer Overflows), sewer failures have been studied much in recent years. This thesis focuses on the blockages of sewer lines, which cause nearly half of the SSOs. A simulation model is developed to analysis efficiency of different inspection programs. A combined factor, which affects the interval time between blockages, is described by two-parameter distribution. Each pipe in the sewer system has characteristic parameters and distribution that is also utilized to simulate the operation of sewer system in the model. Fitting the parameters from historical database, estimated parameters are used to predict blockages. Two methods (Birnbaum-Saunders Distribution estimation and Median estimation) to estimate the parameters are compared from the accuracy and operation time aspects. Meanwhile, failure probability in certain period is calculated from the distribution to support the maintenance schedule, which leads to a probability-based inspection strategy. To ensure the effect of this strategy, a line-by-line inspection strategy in which inspected pipes are selected randomly is also studied. The results show that the strategy with highest inspection efficiency is the probability-based one with parameters estimated from BSD estimation method. Moreover, economic analysis of the strategies is studied to optimize the capital investment of maintenance and the civil penalties regulated by EPA

Investments In Information Technology, Organizational Slack, And Economic Productivity

From a resource-based view (RBV), information technology (IT) investments affect organizational slack resources and therefore influence firm economic productivity. In this study, we develop a framework and test the relationship between economic productivity and organizational slack through an examination of 9 years financial data of 106 U.S. listed companies. Each variable has been tested for three stages of IT investments. Our results suggest that organizational slack resources increase after IT investments which later are consumed and converted into economic productivity

Exploring the Ways of Integrating Traditional Chinese Medicine Culture with the Civic Education of College Students

Traditional Chinese Medicine is the crystallization of Chinese people’s exploration of the way of life and health for thousands of years, which contains rich philosophical ideas, medical theories and treatment methods. Therefore, by clarifying the connotation and significance of the excellent traditional culture of TCM and analyzing the realistic dilemmas in its integration into the civic education of colleges and universities, we aim to enhance the ideology and affinity of the teaching of TCM on the basis of insisting on diversified teaching measures and aligning with the teaching objectives, so as to achieve the integration of TCM culture into the whole process of civic education of young students

Forsythiaside A inhibits hydrogen peroxide-induced inflammation, oxidative stress, and apoptosis of cardiomyocytes

Purpose: To investigate the effect of forsythiaside A on heart failure.Methods: An in vitro cell model of myocardial injury was established by incubating H9c2 primary cardiomyocytes with hydrogen peroxide (H2O2). Apoptosis was measured by flow cytometry. Expression of inflammatory factors, including tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), was determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and enzymelinkedimmunosorbent assay (ELISA). Oxidative stress was evaluated by measuring malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) levels by ELISA.Results: Incubation with H2O2 increased H9c2 cell apoptosis (p < 0.001). Treatment with forsythiaside A reduced Bax expression and enhanced Bcl-2 expression which suppressed apoptosis of H2O2- induced H9c2 cells. Forsythiaside A also attenuated the H2O2-induced increase in TNF-α and IL-6expressions in H9c2 cells (p < 0.001). The H2O2-induced increase in MDA and decrease in SOD and GSH-Px in H9c2 cells were reversed by treatment with forsythiaside A. IκBα protein expression was downregulated, whereas p65 phosphorylation (p-p65), p-IκBα, nuclear factor erythropoietin-2-related factor 2 (Nrf2), and heme oxygenase 1 (HO-1) were upregulated in H2O2-induced H9c2 cells. Forsythiaside A increased IκBα, Nrf2, and HO-1 expression and decreased p-p65 and p-IκBα expression in H2O2-induced H9c2 cells.Conclusion: Forsythiaside A exerts anti-inflammatory, anti-oxidant, and anti-apoptotic effects against H2O2-induced H9c2 cells through inactivation of NF-κB pathway and activation of Nrf2/HO-1 pathway. These results support the potential clinical application of forsythiaside A for the treatment of heart failure

MotionBERT: A Unified Perspective on Learning Human Motion Representations

We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. The motion representations acquired in this way incorporate geometric, kinematic, and physical knowledge about human motion, which can be easily transferred to multiple downstream tasks. We implement the motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network. It could capture long-range spatio-temporal relationships among the skeletal joints comprehensively and adaptively, exemplified by the lowest 3D pose estimation error so far when trained from scratch. Furthermore, our proposed framework achieves state-of-the-art performance on all three downstream tasks by simply finetuning the pretrained motion encoder with a simple regression head (1-2 layers), which demonstrates the versatility of the learned motion representations. Code and models are available at https://motionbert.github.io/Comment: ICCV 2023 Camera Read