Reviewerly/exHarmony huggingface dataset:
Neighbor2vec: An Efficient Method for Graph Embedding

There are many challenges in node representations in large-scale networks with both the node's property well represented and the network's structure involved. Many works use “walks” generated by walk-based sampling strategies to generate latent neighborhood representations for the nodes in networks by treating them as the equivalent of sentences. However, these walk- based sampling strategies do not represent the neighborhood relationship sufficiently because only two neighbors of one central node are sampled in one walk, which tends to be insufficient for the nodes that maintain a large number of neighbors. Besides, the existing works ignore the total structure of the network. In this paper we propose neighbor2vec, a neighbor-focus sampling strategy based algorithm, a framework to collect the structure information through information propagation from the node to its neighbors. Here we argue that neighbor2vec is simple and effective to enhance the equality and scalability of graph embedding, and to outperform the abilities of the existing state-of-the-art methods. We demonstrate that neigh-bor2vec's latent representations on three link prediction and three node classification tasks for graph datasets such as ogbl-ppa, ogbl-collab, ogbl-citation2 ogbn-arxiv, ogbn-products as well as ogbn-proteins, Neighbor2vec's embeddings can promote accuracy scores to 10.8 percent, which is higher compared with current sota competive methods, and therefore can have advantages over almost all baseline methods as well as GNN frameworks in the experiments.
Face Recognition based on Sample Augment and Convolutional Neural Network

In recent years, face recognition has been one of the hot spots in computer vision research, and many methods have been proposed. The deep learning method needs a large number of samples for training. To solve this problem, this paper proposes a method based on eigen subspace perturbation. This method uses matrix decomposition technology to generate virtual face images, so as to achieve the purpose of sample augment. The model is trained and evaluated on Olivetti dataset. Experimental results show that the algorithm proposed in this paper performs a higher recognition accuracy, which provides an idea for deploying high-precision face recognition methods in small sample scenarios.
A Single-view Camera Calibration Method Using Scene Geometry

In the process of camera calibration, when the scale of the object in the image is unknown, the calibration is completed by gradually solving the camera parameters, and the 3D reconstruction experiment of the scene is realized by using the calibration results. The experiment of 3D reconstruction of the scene is realized. The basic idea is to calibrate by two mutually orthogonal four grid pattern reference objects, and use the three groups of parallel lines existing in the scene to solve the vanishing point of the corresponding direction of each group of parallel lines, and then determine the coordinate of the cardinal point according to the properties of the vanishing point. In the next step, the constraint equation system is established by the three-point collinear principle of the straight line, and the intrinsic parameters of the camera are obtained by decomposing the matrix eigenvalues. In this paper, the algorithm implementation process is designed, and a large number of simulation experiments are carried out to show that the method has high robustness. The real image experiments also show that the proposed method has high accuracy and meets the requirements of 3D reconstruction of objects in the actual environment. has a certain use value.
A Feature Reinforcement Method Based on Discrete Fourier Transform

Complex evidence theory (CET), which extends Dempster Shafer theory to a higher dimensional space, is well studied these days. In CET, how to generate CBBA is crucial for uncertainty reasoning, and it is still an open issue. To be specific, CBBA can be generated through data in real number field or in the complex number field. For the data in real number field, it is necessary to mapping it to complex number field. Meanwhile, it is better to reinforce features of data in the process of mapping. In this paper, a discrete Fourier transform-based feature reinforcement method is proposed to map the data from real number field to complex number field. What's more, we propose a novel reinforcement measurement, similarity indices, to quantify the level of the feature reinforcement. The experiments illustrate that the discrete Fourier transform-based feature reinforcement method achieves good performance, which can be beneficial to pattern recognition to some extent.
Traffic Sign Recognition and Detection based on Improved YOLOv5

In order to solve the problem of identifying small targets and occluded traffic signs in the field of assisted driving, this paper uses the deep learning network YOLOv5 model after optimizing the loss function to realize the recognition of traffic signs. First, 45 categories in the TT100K traffic sign dataset are selected as the total number of categories in this experiment. Because the number of pictures in some categories is relatively small, we will complete them, and finally obtain a dataset with a more balanced amount of data, and then use the optimized The YOLOv5 model is trained on this dataset and detects its classes. On the supplemented TT100K data set, comparing the indicators before and after optimization, the results of the optimized YOLOv5 applied to traffic signs have a certain improvement in the mAP, precision and recall rate, and have certain advantages compared with the mainstream target detection algorithms at the present stage. It has certain practical significance for improving driving safety and promoting the development of driverless technology.
Complex Belief KL Divergence in Complex Evidence Theory

A generalized Dempster-Shafer evidence theory, known as complex evidence theory can explain regularly fluctuating data and offer a better framework for uncertain information representing and processing. In this study, we propose a probabilistic transformation method to transform the complex basic belief assignments of complex evidence theory into probability distributions. Additionally, we propose a generalized Kullback-Leibler divergence to measure the discrepancy of two complex basic belief assignments. Besides, we prove the properties of the proposed divergence and validate its availability by numerical examples.
Edge-based Dynamic Data Integrity Checking Scheme for IoT

The increasing data volume causes a challenging issue when storing the data locally in Internet of Things. Therefore, outsourcing the data to cloud service provider is treated as a potential solution for its low cost, high availability and scalability. In this case, users lose control to the outsourced data, facing severe data integrity challenge. Ensuring the integrity of outsourced data is an essential requirement for cloud storage. As the widespread of the cloud-edge framework, the conventional data integrity checking schemes cannot support the offline model and the scalability. In this article, we suggest a novel publicly verifiable edge-based data integrity checking scheme for IoT devices in a decentralized edge-cloud storage scenario, to satisfy the dynamic requirement in scalable outsourcing storage. Through homomorphic verifiable tags, our proposed scheme can achieve the aggregated blockless verification to improve the efficiency on checking. By taking advantage of cloud-edge architecture, our proposed scheme can be further improve the computation and communication efficiency on user side. Also, a comprehensive security analysis is given over the security properties of our proposed data integrity checking scheme. Finally, the performance evaluation conducted under the simulated dataset is provided. The results are shown from the theoretical and experimental aspects to indicate that our proposed scheme is acceptable in the practice.
TSP Image Restoration Model Based on NCC Correlation

When restoring a large number of image fragments, the use of automatic splicing technology can improve the efficiency of image restoration. This paper constructs a TSP image restoration model based on NCC correlation, and proposes different restoration schemes for different image restoration scenarios. The modeling is carried out according to the process of image preprocessing, feature matching, problem conversion and image stitching. Firstly, each small photo is converted from RGB color space to HSV color space, and the V monochromatic channel image is separated, then the gray value matrix is constructed. Secondly, the edge feature vector of the picture is extracted and the NCC edge correlation matrix between the pictures is calculated. Finally, according to the correlation of the upper and lower azimuth between the pictures, the small photos are classified in columns, and then the column stitching and row stitching are completed in turn. In addition, according to different types of small photos, such as partial repetition, different sizes, more fragments and so on, this paper obtains the restored images through K-means clustering algorithm and image enhancement processing. The image restoration system constructed in this paper has better processing effect and higher efficiency.
Graph Convolution Networks based on Higher-order Trustable Neighbors

Graph representation learning enriches the feature representation of nodes by aggregating the features of neighbors. Traditional graph representation learning mainly utilize 1-hop or 2-hop neighbor information to represent node features. However, with the continuous expansion of graph structure, feature extraction is becoming more difficult. Therefore, we consider to aggregate the higher-order trustable neighbors of nodes. The extraction of trustable neighbors depends on the calculation of similarity, and those greater than the similarity threshold are regarded as higher-order trustable neighbors. Furthermore, we add the attention coefficients to the original adjacent matrix and the higher-order trustable matrix to enrich the interaction information. Besides, we set a constraint threshold to explore the contribution of higher-order trustable neighbors. Eventually, the graph convolution network(GCN) with diverse attention is adopted to extract and represent the features of nodes. To verify the efficiency of the proposed algorithm, we have carried out the node classification task on three real citation datasets, and compared it with other excellent node classification baselines, which comes to a conclusion that higher-order trustable neighbor convolution is crucial.
Equipment Fault Entity Recognition Based on Scan Filtering with Dual Neural Networks

A wide range of fault-bearing information is generated during equipment health monitoring tasks, but this information usually exists in the form of text in different stages, which leads to the inefficient use of this information. In addition, there are few studies on information extraction methods in the equipment failure field, and the entity nesting in the field of equipment failure is so severe that most existing named entity recognition (NER) methods don't apply. To address this problem, this paper proposes a dual neural network entity recognition model based on scan filtering for identifying entities in equipment fault texts. The method incorporates a domain dictionary to increase the granularity of text sequences in terms of words, uses sliding windows to extract sequence fragments, and filters sequence fragments by rules to reduce the proportion of negative samples. And feature encoding of sequence fragments using the pre-trained model of bi-directional encoder representations from transformers (Bert), fusion extraction of sequence features using bidirectional long-short-term memory (BiLSTM) network, and convolutional neural network (CNN) to predict the entity labels of sequence fragments. It has been experimentally demonstrated that the model outperforms the baseline model in the equipment faulty corpus.
Detection and Counting of Dense Objects in Plateau Environment

For improved the accuracy of object recognition under specific plateau conditions, enhance the application scope of artificial intelligence, and reduce unnecessary personnel consumption, this paper is based on yak recognition calculation, so that artificial intelligence can still be well applied in the case of dense objects. Based on the object detection task, this paper improves the backbone of the network. In addition, the intelligent counting function is added to give better play to the recognition performance of the network in the case of dense objects. In order to achieve better feature extraction and accurate counting of objects in images, this paper proposes a new attention mechanism that parallel attention mechanism. We embed the parallel attention mechanism into the YOLOX network to test and evaluate it on our own collected dataset. When IoU threshold is 0.5 and 0.75, the obtained mAP is 0.54% and 1.42% higher than YOLOX respectively. We also compare the detection results after embedding the convergence block attention model in YOLOX and the results showed that our algorithm outperformed it by about 0.5% and 0.57%. And the AP values of each species are higher than them.
An Algorithm for Automated Search Model with Pruning Structure based on Big Data

A compressed pruning scheme for convolutional neural networks based on automated search is proposed to address the problem that convolutional neural networks consume many computational resources under large data. First, the target constraint is added by hyperparameters to reduce the combination. Then the search process is viewed as an optimization problem, and the pruning structure is searched and optimized automatically by using the sparrow search algorithm. The algorithm improves model compression efficiency by determining the number of channels in each layer of the neural network and avoids rule-based selection. Experimental datas show that the parameter compression rate reaches 55.28% and the FLOPs are reduced by 58.57% on the ResNet-56 dataset; the parameter compression rate reaches 80.61% and the FLOPs are reduced by 70.34% on the VGG-16 data.
Accent Conversion using Neural Style Transfer

Accent conversion is a useful tool for individuals who want to communicate effectively in a language that is not their native tongue. Accent conversion is a crucial problem in speech processing, where speech from one accent is transformed into another. The ability to convert accents is becoming increasingly important. This paper proposes a novel approach for accent conversion using neural style transfer and check whether this method can perform well. One way to achieve this technique is by maintaining the content of an audio signal and transferring the style of another signal onto it. The proposed method involves a specific type of architecture called encoder-decoder. Also included a style loss term, which is a technique that trains the network to recognize and replicate the targeted accent style. And train the network on a dataset of speech samples from different accents and evaluate its performance on a separate test set. The proposed method successfully converts speech samples from different accents while maintaining their original content and emotional expression.
A Real-time Semantic Segmentation Network with Multi-path Structure

The real-time semantic segmentation network has the problem of insufficient semantic information extraction, and the network structure has the problem of insufficient fusion of different branch information, which leads to the reduction of segmentation accuracy. We propose a real-time semantic segmentation model combined with a feature extraction module to address the problem of insufficient semantic information extracted from feature maps, and perform feature layer fusion before the final fusion of the two branches. First, Select feature layers from two different branches of the dual branch structure for fusion to strengthen the semantic information extraction ability of semantic branches; Secondly, a new module is designed on the semantic branch, which uses a multi-path structure to obtain more semantic information using convolution of different, and aggregates contextual information in different regions, improving the semantic segmentation performance of the network. For 1024x2048 resolution input, we have confirmed through related experiment that the improved method achieves 74.1% MIOU accuracy on the Cityscapes dataset.
Small object detection based on WavesNET

In recent years, object detection technology has developed rapidly, especially the FPN has been widely adopted in production application and academic research. Compared with the traditional network, the accuracy of EfficientDet based on the FPN structure has reached 55.1% on the COCO dataset, and the number of parameters and computation cost is also greatly reduced by 9–25 times. However, the EfficientDet training process does not integrate the upper and lower level feature information quickly enough, especially depending on the multi-layer BiFPN overlap and increasing input size and channel number, therefore, we proposed WavesNet based on EfficientDet. First, we integrated seven channels on the basis of BiFPN to make all levels of the fusion more quickly and effectively, and then applied the ComC (Combine Convolution) extraction method in the initial phase of feature extraction. In addition, the EfficientDet fast normalization formula was improved to ensure the numerical stability of the features. Experimental results show the WavesNet has 30% faster convergence rate and 0.5% higher accuracy on COCO dataset than the EfficientDet. The detection effect on small targets is obvious.
MFA: A Novel Attention Module for Old Image Restoration

Restoration of old images acquired from repositories of historical photographic and film archive is of key importance for the preservation, presentation and transmission of the legacy of past cultures to the coming generations. These old images are often subject to severe blending degradation, and most related methods are less effective in restoring larger structural degradations like vertical stripes. To better restore multiple mixed degradations in old images, we collect thousands of real old images and synthetic degradation images to build our dataset. Besides, a novel Mixed Feature Attention (MFA) module, which includes channel attention, spatial attention and pixel attention mechanism, is proposed to optimize the restoration of structural degradations and ensure that the restored content of structural degradations can match the surrounding pixels. And we creatively integrate the MFA into a triple domain translation network. The experimental results show that our method can achieve the most advanced performance in terms of quantitative, qualitative comparison and user study, and has more real and natural repair result specially for structural degradation.
Semi-supervised Active Learning Based on Semantic-aware Crop Consistency

Reducing labeling expenses is the main goal of active learning. However, due to the problem of cold start, the poor sample selection of the initial pool decreases the performance of many active learning methods. Besides, pure active learning methods fails to utilize the information of the unlabeled samples for model training. To tackle the problems, we combine active learning with consistency regularization based on cropping of the semi-supervised learning. To reduce the possibility that random crop may lead to false positives and thus poor understanding of the cropped content by the model, we use a localization module that is semantically aware to significantly lower false positives. The combination of both semantic-aware crop consistency-based semi-supervised learning and active learning can maximize the classification performance with minimal human cost. The method is evaluated using the three distinct image classification datasets CIFAR-10, CIFAR-100, and SVHN. The results illustrate the superiority of our method over competing methods.
Underwater Image Enhancement via Residual Dense Block and Feature Pyramid Attention

When light travels underwater, as a result of absorption and scattering, underwater images show color casts and low contrast. Recent years have witnessed a boom in approaches of underwater image enhancement which based on deep learning methods. However, the existing methods do not utilize the rich hierarchical features in the degraded underwater images. We propose a new underwater image enhancement approach to address these problems, which contains the residual dense block and the feature pyramid attention. The abundant hierarchical features in degraded underwater images are extracted through densely connected convolutional layers. Besides, the feature pyramid attention provides accurate pixel level attention for high-dimensional features of the network. Our proposed approach can remove color casts and improve image contrast effectively, outperforming other traditional and deep learning methods on a real-world dataset.
MR2D: Multiple Random Masking Reconstruction Adversarial Detector

To perceive and prevent adversarial examples (AEs) from fooling deep neural networks, many adversarial detectors learn various distributions to distinguish them from benign ones. However, those methods are limited by the target model structures and prior experiences. To solve the above problems, we proposed an adversarial detector named MR2D (Multiple, Random Masking, Reconstruction Adversarial Detector) with randomness and redundancy mechanisms. The core idea of MR2D is to judge multiple randomly masked and reconstructed input images with detection strategies. Based on the Dimpled Manifold Model theory, the more adversarial perturbations are masked, the more reconstructed patches will be cast on the image manifold, and the higher possibility of predicting correct labels. After several times repeating the process, the predictions of reconstructed AEs may be different, while benign ones usually are consistent. Through well-designed detection strategies, the MR2D can quickly distinguish AEs by three-step judgments. On the CIFAR-10 dataset, the experimental results show that the MR2D achieves better overall performance under the metrics of AUROC, AUPR, and ACC, when facing eight classical types of AEs each with three different attack strengths, than other adversarial detectors. Moreover, the MR2D can perform as a plugin to enhance the adversarial robustness of other methods, such as combining it with multiple denoise techniques.
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance

Due to the problem of performance constraints of unsupervised video object detection, its large-scale appli-cation is limited. In response to this pain point, we propose another excellent method to solve this problematic point. By incorporating motion characterization in unsupervised video object detection, detection accuracy is improved while reducing the computational amount of the network. The whole network structure consists of dual-stream network, motion guidance module, and multi-scale progressive fusion module. The ap-pearance and motion representations of the detection target are obtained through a dual-stream network. Then, the semantic features of the motion representation are obtained through the local attention mechanism in the motion guidance module to obtain the high-level semantic features of the appearance representation. The multi-scale progressive fusion module then fuses the features of different deep semantic features in the dual-stream network further to improve the detection effect of the overall network. We have conducted numerous experiments on the three datasets of DAVIS 16, FBMS, and ViSal. The verification results show that the proposed method achieves superior accuracy and performance and proves the superiority and robustness of the algorithm.