Reviewerly/exHarmony huggingface dataset:
Garment Segmentation Network Based on Texture and Semantic Decoding Module

Abstract With the great development of the apparel e-commerce industry, more and more people are engaged in this field. In reality, clothing is easily affected by background, perspective, occlusion, and other factors, so clothing segmentation is not accurate enough. This paper proposes a clothing segmentation network based on the texture and semantic decoding module of clothing. Based on the swing transformer network, this network proposes a clothing texture analysis module (CTAM) and a clothing semantic decoding module (CSDM). In the coding process, CTAM extracts clothing texture features to enhance spatial information, while CSDM uses mixed attention to enhance image context information during decoding to improve the accuracy of clothing segmentation. The experimental results show that compared with the original model, the average pixel accuracy of the proposed method on the LIP data set is increased by about 2%, and the average crossover ratio is increased by about 1.5%. The segmentation network significantly improves indistinguishable garment boundaries and the accuracy of garment image segmentation.
Natural scene text detection algorithm based on the regional proposal

Abstract Text detection in natural scenes is widely used in various fields. The combination of deep learning models and text detection improves the accuracy and efficiency of detection. Its algorithm is also endless, with continuous optimization and improvement. On this basis, some algorithms for text detection in natural scenes are summarized from the two aspects of the Faster R-CNN method and SSD method based on regional suggestion. These algorithms and experimental data are analyzed and compared to further summarize the challenges faced by text detection in natural scenes and the direction of improvement.
Quantum and Condor-Based Brainstorming Optimization Algorithm for NOx Prediction

Abstract This paper proposed a quantum bald eagle brainstorm (QBBSO) based on quantum initialization and combined with the bald eagle optimization algorithm in light of the original Brain Storm Optimization (BSO) algorithm’s strong local search ability, which would result in local optimization, a poor optimization effect, and difficult development. To accomplish the randomness of the number and increase the randomness of the population, we first modified the initialization method of the original brainstorming population, introduced the idea of quantum code, and then translated the binary numbers 0 and 1 to the decimal number. To achieve the best outcome, the original step size formula was employed for global selection, local search, and final selection using the vulture search method. The original BSO was then optimized to attract more global individuals. The algorithm versions were compared using the common benchmark function test. The findings demonstrated that QBBSO had a greater capacity for global search and a faster convergence speed. This research also applied the QBBSO algorithm to the long-term and short-term memory network (LSTM) to forecast the NOx concentration in the boiler, further demonstrating the algorithm’s superiority in real-world settings.
A Rotation-Invariance Face Detector Based on RetinaNet

Abstract The use of deep convolutional neural networks has greatly improved the performance of general face detection. For detecting rotated faces, the mainstream approach is to use multi-stage detectors to gradually adjust the rotated face to a vertical orientation for detection, which increases the complexity of training as multiple networks are involved. In this study, we propose a new method for rotation-invariant face detection, which abandons the previously used cascaded architecture with multiple stages and instead uses a single-stage detector to achieve end-to-end detection of face classification, face box regression, and facial landmark regression. Extensive experiments on FDDB in multiple orientations have shown the effectiveness of our method. The results demonstrate that our method achieves good detection performance and the detection accuracy of our method even exceeds that of other rotated face detectors on the front-facing FDDB dataset.
3D Human Pose Estimation Based on Transformer

Abstract Currently, 3D human pose estimation has gradually been a well-liked subject. Although various models based on the deep neural network have produced an excellent performance, they still suffer from the ignorance of multiple feasible pose solutions and the problem of the relatively-fixed input length. To solve these issues, a coordinate transformer encoder based on a 2D pose is constructed to generate multiple feasible pose solutions, and multi-to-one pose mapping is employed to generate a reliable pose. A temporal transformer encoder is used to exploit the temporal dependencies of consecutive pose sequences, which avoids the issue of relatively-fixed input length caused by temporal dilated convolution. Adequate experiments indicate that our model achieves a promising performance.
Application of Improved Genetic Algorithm and Ant Colony Algorithm in Multi-objective Path Planning

Abstract Due to the shortcomings of GA in path planning, there are too many control variables and it is easy to fall into local areas. We introduce the idea of a gene bank and store the new chromosomes generated each time in the gene bank. The idea of linear regression is proposed to predict the probability of crossover and mutation of the next generation. At the same time, a method of increasing chromosome diversity is proposed, which avoids the problem that it is difficult to set the control variable parameters reasonably and fall into local optimum when the GA is applied in path planning. To overcome the shortcomings of ACO in path planning, an ACO with improved heuristic function and transition probability was proposed, and the method of the real-time environment was given. Through the traveling salesman problem, the improved GA and ACO are combined to apply to path planning. The experimental results show that the fusion of GA and ACO in this paper is superior to ordinary GA and ACO in all aspects of path planning.
Design of Intelligent Voice Greenhouse System Based on STM32 MCU

Abstract The traditional agricultural management mode no longer meets the needs of modern society, necessitating the development of modern agriculture. However, the limited education level in rural areas and the difficulty in popularizing complex IoT systems and internet platforms pose challenges to the development of intelligent agriculture. To address this, this paper proposes an intelligent voice greenhouse system based on a traditional STM32 single-chip microcomputer. The system utilizes various sensors to monitor environmental parameters and enable voice interactive control. This simple and practical approach promotes the use of smart agriculture, making it more accessible and easier to use. Physical testing has confirmed the effectiveness of this method, which will contribute to the development of intelligent agriculture in the future.
Improved MobileViT: A More Efficient Light-weight Convolution and Vision Transformer Hybrid Model

Abstract Convolutional neural networks (CNNs) utilize spatial induction bias to learn local features of images, while vision transformers (ViTs) can learn global representations of visual information through self-attention. However, due to ViT’s large number of parameters, it is challenging to apply them to mobile vision tasks. Based on Conv-ViT light-weight hybrid model MobileViT, this paper proposed the improved backbone network and transformer block to improve the applicability. Firstly, the MV2 block in MobileViT is substituted with an improved depth separable convolution and linear activation function. Secondly, we proposed a novel self-attention to replace the original self-attention of the transformer block in MobileViT, which reduces computational load and enhances global representation by enabling information interaction between different windows. Experimental results demonstrate that the improved model’s accuracy in image classification is 2.2% higher than that of MobileViT on the CIFAR-100 dataset.
A variable adversarial attack method based on filtering

Previous works have pointed out that deep learning models are vulnerable to adversarial examples crafted by adding negligible confusing noise to clean images. However, adversarial examples are hard to transfer to attack other defense models or do not conform to human-imperceptible under the black-box setting. To bridge this gap, a variable adversarial attack method based on filtering called VFI-FGSM is proposed. Variable Step Size Method (VSSM) is designed to adaptively control perturbation to avoid overfitting, the Filtering based Iterative Fast Gradient Sign Method (FI-FGSM) is explored to enhance robustness of attack, an auxiliary loss of high-level representation is added to get a more precise gradient direction and improve the attack success rate, and a new tactic named the contribution for reducing perturbation size(CRPS) are presented to measure the performance of attack algorithms in the paper. The results show that scheme proposed increases the attack success rate by an average of 14.36% in ensemble attack method and 7.9% on combined trained model for defense models in black-box setting. Fortunately, the noise is perceptually small in both L1 and L2 metrics.
A fault tolerance aware green IoT workflow scheduling algorithm for multi-dimensional resource utilization in sustainable cloud computing

In recent times, the distributed cloud computing landscape has witnessed a remarkable surge in processing vast amounts of data and the crucial need to maintain service level agreements (SLAs) between providers and consumers. In this dynamic environment, cloud resources are inherently multidimensional, encompassing computing machines and communication connections susceptible to failures and energy-related considerations. However, given two-objective energy and reliability optimization, existing time allocation policies focus primarily on optimizing a single intent, which leads to system degradation in environments that generate problematic constraints from executable processing units. To handle the shortcomings of the application placement policies, we suggest a solution in the form of a three-phase bi-objective workflow scheduling issue called (Bi-OWSP). This three-phase approach aims to optimize workflow scheduling by simultaneously considering energy and reliability as two vital objectives. To achieve this, we employ two distinct algorithms. First is the stepwise dynamic voltage and frequency scale algorithm ensures energy efficiency by calculating optimal frequencies, reducing energy usage, and minimizing task mapping time. The second algorithm is the reliability-conscious heterogeneous fault tolerance approach, which emphasizes avoiding high-deficit servers and communication links to enhance system reliability. Furthermore, we introduce an energy-aware stepwise reliability maximization algorithm, which intelligently selects the best combination of task-server pairs to achieve energy minimization and reliability maximization. Through extensive simulation experiments on artificial and real-world workflow applications, we demonstrate the significance of Bi-OWSP in providing superior energy-reliability compensation solutions compared to competing algorithms.
Deep multi-task learning for nephropathy diagnosis on immunofluorescence images

As an advanced technique, immunofluorescence (IF) is one of the most widely-used medical image for nephropathy diagnosis, due to its ease of acquisition with low cost. In practice, the clinically collected IF images are commonly corrupted by blurs at different degrees, mainly because of the inaccurate focus at the acquisition stage. Although deep neural network (DNN) methods achieve the great success in nephropathy diagnosis, their performance dramatically drops over the blurred IF images. This significantly limits the potential of leveraging the advanced DNN techniques in real-world nephropathy diagnosis scenarios.This paper first establishes two IF databases with synthetic blurs (IFVB) and real-world blurs (Real-IF) for nephropathy diagnosis, respectively, including 1,659 patients and 6,521 IF images with various degrees of blurs. According to the analysis on these two databases, we propose a deep hierarchical multi-task learning based nephropathy diagnosis (DeepMT-ND) method to bridge the gap between the low-level vision and high-level medical tasks. Specifically, DeepMT-ND simultaneously handles the main task of automatic nephropathy diagnosis, as well as the auxiliary tasks of image quality assessment (IQA) and de-blurring.Extensive experiments show the superiority of our DeepMT-ND in terms of diagnosis accuracy and generalization ability. For instance, our method performs better than nephrologists with at least 15.4% and 6.5% accuracy improvements in IFVB and Real-IF, respectively. Meanwhile, our method also achieves comparable performance in two auxiliary tasks of IQA and de-blurring on blurred IF images.In this paper, we propose a new DeepMT-ND method for nephropathy diagnosis on blurred IF images. The proposed hierarchical multi-task learning framework provides the new scope to narrow the gap between the low-level vision and high-level medical tasks, and will contribute to nephropathy diagnosis in clinical scenarios. The diagnosis accuracy and generalization ability of DeepMT-ND are experimentally verified to be effective over both synthetic and real-world databases.
FePN: A robust feature purification network to defend against adversarial examples

Deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial attacks. Existing defenses can defend against a variety of adversarial examples. However, as the perturbation budget used to create adversarial examples increases, their adversarial robustness decreases dramatically. To solve this problem, we develop a Feature Purification Network (FePN), which is based on the fact that adversarial examples are closely associated with non-robust features of data. Specifically, an adversarial learning mechanism is proposed to learn robust features by removing non-robust features from inputs. Meanwhile, two linear branches and a discriminator are designed to reconstruct high-quality natural images by exploiting robust features. FePN is a preprocessing approach that can be utilized to safeguard other models without altering them. We conduct a series of experiments on MNIST, CIFAR-10 and ImageNet. Experimental results have proven that FePN can provide effective protection compared to previous state-of-the-art approaches, as well as superior robustness against attacks with considerable perturbations.
Three-Dimensional Sphere Recognition and Tracking Based on YOLO

Traditional art exhibitions are usually dominated by relatively static displays such as text, pictures and common multimedia technology. Subject to technical limitations, the exhibition means are relatively simple and the content is relatively thin, which cannot fully meet the exhibition needs of the organizers, nor can it mobilize the enthusiasm of the visitors, and fails to fully show the communication of the exhibition. Therefore, an object detection model based on You Only Look Once(YOLO) network is proposed in this paper to recognize and track the spheres made by felt process. First, the YOLO network was pre-trained using the open source data set, and then the pre-training model was fine-tuned according to the felt sphere image training set. Before fine tuning, the k-means clustering algorithm was used to cluster the marking information of the sphere training set made by felt process. Secondly, for the display of the effect after recognition, OpenCV image processing is used for image special effect processing of the specific recognition area. Through the experimental results, the object detection based on YOLO network proposed in this paper can reach 80.95% in detection accuracy [email protected]:0.95 and detection speed up to 20ms, showing excellent performance in detection accuracy and detection speed. It can fit the background interactive display effect of felt art well.
Frequency-Split Inception Transformer for Image Super-Resolution

Transformer models have shown remarkable effectiveness in capturing long-range dependencies and extracting features for single image super-resolution. However, their deployment on edge devices is hindered by their high computational complexity. To address this challenge, we propose Inception Swin Transformer (IST), a novel model that leverages frequency domain separation to reduce redundant computations.In IST, we exploit the strengths of both CNN-based networks and Transformer variants to handle high-frequency and low-frequency features, respectively. By dynamically utilizing frequency factors to separate feature maps, IST ensures that different components are processed appropriately. Additionally, IST maintains a balanced trade-off between model speed and performance by gradually reducing the proportion of high-frequency components.Our experiments demonstrate that IST effectively reduces the FLOPs while preserving high performance. The combination of Transformers’ accuracy and CNN variants’ efficiency enables IST to significantly reduce computational strain without compromising quality. Comparative analysis reveals that IST outperforms other models, achieving superior results with less FLOPs.
Enhancing Students’ Writing Quality in a Blended Learning Environment

This study aims to explore the best implementation of Blended Learning Model to enhance students’ writing quality. This is a Classroom Action Research where Blended Learning Model was applied using two different media. The mean score result show that students’ writing quality improve from 67.83 to 75.74 at the first cycle, where videos was applied in Blended Learning Model. At the second cycle where was video was switched to PowerPoint, the students’ writing quality reach the mean score of 83.10. These findings suggested that the application of PowerPoint in Blended Learning could address the students’ problem in term of word choice, generic structure, and grammar better than videos. Students also showed positive attitude, response, as well as active participation in learning. It shows that the application of PowerPoint in Blended Learning Model could maintain students’ attention that could give better result in learning writing. Therefore, the media selection in Blended Learning environment should consider its ability in maintaining students’ attention.
Reconstructing 3D Shapes as an Union of Boxes from Multi-View Images

The task of reconstructing object shapes from input images has become increasingly important in various fields, such as computer vision, robotics, augmented reality, video games, and autonomous vehicles. While approaches for reconstructing shapes with varying levels of detail have been proposed, balancing representation accuracy and model complexity remains a challenge. To address this challenge, we propose an end-to-end approach for reconstructing object shapes from multiple images using a union of box primitives. Our approach offers a simpler and more efficient 3D representation of objects without the need for intermediate products such as voxels, resulting in faster inference times. Additionally, we introduce an auxiliary task to aid in learning how to extract and transform spatial features from images without requiring camera calibrations. Extensive experiments demonstrate that our method can produce comparable results to approaches that require 3D voxelized input while utilizing only 2D RGB images as input. Furthermore, our method significantly outperforms the aforementioned approaches in terms of inference time.
An Architecture for Dynamic Load Balancing in Cloud Environment

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the Cloud.This research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future.
Survey of the Formal Verification of Operating Systems in Power Monitoring System

The formal verification of the operating systems in power monitoring system is an important means to ensure the security of the operating system in power monitoring system. This paper introduces the verification principles and framework of formal verification of operating systems in power monitoring system, the languages and tools used in formal verification, and some classic projects of formal verification of operating systems. Through the introduction of the related content of the formalization of these operating systems, some ideas and future development trends of the formal verification of the current operating systems are explained. It has completed the verification process, beginning with weak type safety and progressing to functional correctness, proof of the high-level abstract protocol, and modification of the low-level code. These gain from the constant advancement and refinement of tools and technologies for formal verification of operating systems, but it is also subject to formal verification tools and techniques, and cannot genuinely go towards the last practical link of production. The automated research on formal verification tools and technologies will continue to be a significant advance in operating system formal verification.
An Empirical Study on the Correlation between Neuron Coverage and Code Coverage

Deep learning techniques have been widely used in many important areas. Therefore, testing of deep neural network is very important. In recent years, a few neuron coverage metrics have been proposed that are similar to the concept of traditional code coverage metrics. However, it is little known how such neuron coverage metrics are related to the traditional code coverage metrics. In this paper, we propose an automated approach to correlating neuron coverage to traditional code coverage and based on the approach we conduct an empirical study on the correlation between neuron coverage and code coverage. The experimental results confirm that neuron coverage is positive correlate to code coverage in testing individual Java methods. Our result also suggests that we do not have to reach a high neuron coverage because it may request much more test cases than those requested by high code coverage. To the best of our knowledge, we are the first to investigate the correlation between neuron coverage and code coverage.