Reviewerly/exHarmony huggingface dataset:
Robust Hypergraph-Augmented Graph Contrastive Learning for Graph Self-Supervised Learning

Graph contrastive learning has emerged as a promising method for self-supervised graph representation learning. The traditional framework conventionally imposes two graph views generated by leveraging graph data augmentations. Such an approach focuses on leading the model to learn discriminative information from graph local structures, which brings up an intrinsic issue that the model partially fails to obtain sufficient discriminative information contained by the graph global information. To this end, we propose a hypergraph-augmented view to empower the self-supervised graph representation learning model to better capture the global information from nodes and corresponding edges. In the further exploration of the graph contrastive learning, we discover a principal challenge undermining conventional contrastive methods: the false negative sample problem, i.e., specific negative samples actually belong to the same category of the anchor sample. To address this issue, we take the neighbors of nodes into consideration and propose the robust graph contrastive learning. In practice, we empirically observe that the proposed hypergraph-augmented view can further enhance the robustness of graph contrastive learning by adopting our framework. Based on these improvements, we propose a novel method called Robust Hypergraph-Augmented Graph Contrastive Learning (RH-GCL). We conduct various experiments in the settings of both transductive and inductive node classification. The results demonstrate that our method achieves the state-of-the-art (SOTA) performance on different datasets. Specifically, the accuracy of node classification on Cora dataset is 84.4%, which is 1.1% higher than that of GRACE. We also perform the ablation study to verify the effectiveness of each part of our proposed method.
Image Generation Model Applying PCA on Latent Space

Image generation is an important area of artificial intelligence that involves creating new images from existing datasets. It involves learning the distribution of target images from randomly generated vectors. Like other deep learning models, the image generation model requires a vast refined data set to produce high-quality results. When there is little data, there is a problem that the diversity and quality of generated images are compromised. In this paper, we propose a new generative model that applies PCA to the generator of the least square error adversarial generative network that, in turn, generates high-quality images even with a small data set. Unlike the existing models that generate target data from randomly generated noise, in the proposed method the direction of the image to be generated is guided by extracting the features of the target data through PCA. The results section shows the superior performance of the proposed model against a different number of images in datasets.
MergeTree: a Tree Model with Merged Nodes for Threat Induction

Threat tree model can clearly organize threat induction information and thus is widely used for risk analysis in software assurance. Threat tree will grow to complicated structures, e.g., the number of nodes and branches, when the threat information grows to a huge volume. To extend the scalability of the threat tree model, we propose a tree model with merged nodes so as to largely decrease the number of nodes and branches. The formal model and dedicated algorithms are proposed in details. The experimental results show the practicality of MergeTree. We also formally analyze the soundness and completeness of the proposed model.
An autoencoder-based fast online clustering algorithm for evolving data stream

In the era of Big Data, more and more IoT devices are generating huge amounts of high-dimensional, real-time and dynamic data streams. As a result, there is a growing interest in how to cluster this data effectively and efficiently. Although a number of popular two-stage data stream clustering algorithms have been proposed, these algorithms still have some problems that are difficult to solve in the face of real-world data streams: poor handling of high-dimensional data streams and difficulty in effective dimensionality reduction; a slow clustering process that makes it difficult to meet real-time requirements; and too many manually defined parameters that make it difficult to cope with evolving data streams. This paper proposes an autoencoder-based fast online clustering algorithm for evolving data stream(AFOCEDS). The algorithm uses a stacked denoising autoencoder to reduce the dimensionality of the data, a multi-threaded approach to improve response speed, and a mechanism to automatically update parameters to cope with evolving data streams. The experiments on several realistic data streams show that AFOCEDS outperforms other algorithms in terms of effectiveness and speed.
Research on Constant Perturbation Strategy for Deep Reinforcement Learning

The development of attack algorithms for deep reinforcement learning is an important part of its security research. In this paper, we propose a deep reinforcement constant perturbation strategy approach for deep reinforcement learning with long-range time-series dependence from the perspective of the sequence of interaction between an agent and its environment.The algorithm is based on a small amount of historical interaction information, and a constant perturbation is designed to disrupt the long-range temporal association of the deep reinforcement learning algorithm based on sensitive region selection to achieve the attack effect.The experimental results show that the constant perturbation based on time series has a good effect, i.e. inducing agents to make frequent wrong decisions and get minimal reward. At the same time, this algorithm still has an attacking effect on the defensively trained agents, and it effectively reduces the number of computations adversarial perturbations.
Quantum kernel subspace alignment for unsupervised domain adaptation

Domain adaptation (DA), the sub-realm of the transfer learning, attempts to deal with machine learning tasks on an unprocessed data domain with the different, but related labeled source domain. However, the classical DA can not efficiently deal with the cross-domain tasks in quantum mechanical scenarios. In this paper, the quantum kernel subspace alignment algorithm is proposed to achieve the procedure of DA by extracting the non-linear features with the quantum kernel method and aligning the two domains with the unitary evolution. The method presented in our work can be implemented on the universal quantum computer with the quantum basic linear algebra subroutines. Based on the algorithmic complexity analysis, the procedure of the QKSA can be implemented with at least quadratic quantum speedup compared with the classical DA algorithms.
Helmet wear detection based on YOLOV5

Safety helmet wearing detection is an important safety inspection task with widespread applications in industries, construction, and transportation. Traditional safety helmet wearing detection methods typically use feature-based classifiers such as SVM and decision trees, but these methods often have low accuracy and poor adaptability. In this paper, we propose an improved helmet detection method that uses a combination of SPD Conv, ASPP and BiFPN structures to increase the perceptual field to ensure maximum feature extraction from the helmet, and can ensure fusion between different feature layers to pass semantic information to deeper neural networks, effectively avoiding information loss and improving the performance of detecting helmets. Experimental results show that our method has a 1% improvement in the average accuracy of detection in the public dataset VCO2007 set compared to YOLOv5, which still allows for real-time detection and meets the needs of industry with some practicality.
A U-Net based Self-Supervised Image Generation Model Applying PCA using Small Datasets

Generative Adversarial Networks (GAN) is a research-based on deep learning technology that synthetically generates, combines, and transforms images similar to the original images. The main focus of GAN existing work has been to improve the quality of generated images and to generate high-resolution images by changing the training scheme or devising more complex models. However, these models require a large amount of data and are not suitable for training with a small amount of data. To address these challenges, this paper aims to improve the quality of images and the stability of training with a small dataset by proposing a novel training method for generating real-world images by using PCA and Self-Supervised GAN. Previously, PCA was applied to DCGAN to generate images with a small dataset, but some images showed poor results. By preparing quantitatively different datasets, we show that the quality of generated image with a small dataset is equivalent, or even better when compared to the quality of the image generated with a large dataset.
Comparative Research on Embedding Methods for Video Knowledge Graph

In the video recommendation scenario, knowledge graphs are usually introduced to supplement the data information between videos to achieve information expansion and solve the problems of data sparsity and user cold start. However, there are few high-quality knowledge graphs available in the field of video recommendation, and there are many schemes based on knowledge graph embedding, which have different effects on recommendation performance and bring difficulties to researchers. Based on the streaming media video website data, this paper constructs knowledge graphs of two typical scenarios (i.e., sparse distribution scenarios and dense distribution scenarios ). Moreover, six state-of-the-art knowledge graph embedding methods are analyzed based on extensive experiments from three aspects: data distribution type, data set segmentation method, and recommended quantity range. Comparing the recommendation effect of knowledge graph embedding methods. The experimental results demonstrate that: in the sparse distribution scenario , the recommendation effect using TransE is the best; in the dense distribution scenario, the recommendation effect using TransE or TranD is the best. It provides a reference for subsequent researchers on how to choose knowledge map embedding methods under specific data distribution.
TIRec: Transformer-based Invoice Text Recognition

A novel invoice text recognition model is proposed. In the past few years, researchers have explored text recognition methods with RNN-like structures to model semantic information. However, RNN-based approaches have some obvious drawbacks, such as the level-by-level decoding approach and the one-way serial transmission of semantic information, which greatly limit semantic information's effectiveness and computational efficiency. In contrast, invoice text has obvious contextual relationships due to its fixed text pattern, the text font in the invoice is more fixed and the complexity of the background is much lower than that of natural scenes. To further exploit these contextual relationships and adapt to the characteristics of invoice text, we propose a new text recognition framework inspired by Transformer [1]. Self-attention-based architectures, in particular Transformer, have been successful in natural language processing (NLP). It has demonstrated powerful semantic information modeling capabilities in NLP. Inspired by its success, we try to apply Transformer to invoice text recognition. Unlike the RNN-based approach, we reduce the parameters of the vision network used to extract image features, use the Convolutional Vision Transformer Attention module to capture the semantic information, and use the Transformer decoding module to decode all characters in parallel. We hope that this Transformer-based architecture can better model the semantic information in invoices while remaining lightweight. Meanwhile, we collected text images of more than 40,000 train invoices, VAT invoices, rolled invoices, and cab invoices. Experiments on the collected invoice text recognition dataset show that our approach outperforms previous methods in terms of accuracy and speed.
Unknown Radar Signals Deinterleaving Based on TCN Network

Radar signals deinterleaving plays a critical role in electronic reconnaissance. Nevertheless, due to the extremely high density of intercepted signal trains and the unknown number of emitters, along with the low probability of interception (LPI), high loss rate, and high spurious rate, the deinterleaving task is becoming more challenging. In this paper, we propose a temporal convolutional network (TCN)-based method for deinterleaving radar signal pulse trains, using only the time of arrival (TOA) parameter without knowing how many emitters there are. Simulation results indicate that the proposed method can still achieve high accuracy in situations with high pulse loss and spurious rates.
ADCapsNet: An Efficient and Robust Capsule Network Model for Anomaly Detection

With the rapid development of the industrial internet of things(IIoT), the anomalies will cause significant damage to the ordinary operation of the industry. Anomaly detection work has increasingly become a hot spot. Although many related kinds of research exist, some problems still need to be solved. This paper proposes an efficient and robust semi-supervised capsule network (ADCapsNet) for anomaly detection by changing the convolution structure to better extract the features of the data and adding a new SecondaryCaps layer to better extract spatial relationships. Besides, we optimize the vector selection for dynamic anomaly detection routing and propose the scoring operation, the modified probability mechanism. The modified probability mechanism can widen the score gap between positive and negative samples. This model can accurately identify and output the spatial relationships. Extensive experiments on four datasets show that the ADCapsNet has good performance in anomaly detection.
A Component for Query-based Object Detection in Crowded Scenes

Query-based object detection, including DETR and Sparse R-CNN, has gained considerable attention in recent years. However, in dense scenes, end-to-end object detection methods are prone to false positives. To address this issue, we propose a graph convolution-based post-processing component to refine the output results from Sparse R-CNN. Specifically, we initially select high-scoring queries to generate true positive predictions. Subsequently, the query updater refines noisy query features using GCN. Lastly, the label assignment rule matches accepted predictions to ground truth objects, eliminates matched targets, and associates noisy predictions with the remaining ground truth objects. Our method significantly enhances performance in crowded scenes. Our method achieves 92.3% AP and 41.6% on CrowdHuman dataset, which is a challenging objection detection dataset.
Research on Natural Scene Vehicle Nameplate Text Detection Based on Improved DBNet

Vehicle nameplate information as the main content of vehicle test, it is an important guarantee for the test quality of automobile testing institutions, and an important basis for the transportation authorities to determine the consistency of vehicle parameter configuration. Aiming at the problems of diverse text distribution, variable scale and complex background in vehicle nameplate detection, this paper proposes a dense connection and feature enhancement based on differentiable Binarization (DBNet) semantic segmentation algorithm. This algorithm uses the Dense Atrous Spatial Pyramid Pooling (DASPP) module to establish the connection between multiple dilated convolutions, capture dense sampling point pixels, and improve the utilization of high-level feature information. Secondly, the Feature Pyramid Enhancement Module (FPEM) is used to enhance the expression ability of the multi-layer feature information output from the backbone network, and the Feature Fusion Module (FFM) is used to fuse the feature information of different scales output from the FPEM, which improves the complementary ability between the features of each layer and obtains more comprehensive feature map information. Finally, the output of the DASPP and the FFM are concatenated to get the final segmentation results. The experimental results show that the improved algorithm can effectively locate the nameplate text area in the complex background. The detection accuracy on the self-defined datasets reaches 90.4 %, which is 2.6 % higher than the original algorithm DBNet.
CIP-ES: Causal Input Perturbation for Explanation Surrogates

With current advances in Machine Learning and its growing use in high-impact scenarios, the demand for interpretable and explainable models becomes crucial. Causality research tries to go beyond statistical correlations by focusing on causal relationships, which is fundamental for Interpretable and Explainable Artificial Intelligence. In this paper, we perturb the input for explanation surrogates based on causal graphs. We present an approach to combine surrogate-based explanations with causal knowledge. We apply the perturbed data to the Local Interpretable Model-agnostic Explanations (LIME) approach to showcase how causal graphs improve explanations of surrogate models. We thus integrate features from both domains by adding a causal component to local explanations. The proposed approach enables explanations that suit the expectations of the user by having the user define an appropriate causal graph. Accordingly, these expectations are true to the user. We demonstrate the suitability of our method using real world data.
KRE: A Key-retained Random Erasing Method for Occluded Person Re-identification

Occluded person re-identification (ReID) is a challenging task in the field of computer vision, facing the problem that the target pedestrians in probe images are obscured by various occlusions. Random Erasing in data augmentation techniques is one of the effective methods used to deal with the occlusion problem, but it may introduce noise into the training process, which affects the training of the model. In order to solve this problem, we propose an novel data augmentation method named Key-retained Random Erasing (KRE) which preserves the critical parts in images for occluded person ReID. Based on the regular Random Erasing, we utilize the naturally generated attention map in Vision Transformers and introduce an adaptive threshold selection method to detect the key areas of the image to be augmented. The complexity of the training samples can be improved without losing the key information of the images by reserving the key areas in Random Erasing process, which can finally alleviate the occluded person ReID problem. Validating the proposed method on occluded, partial and holistic ReID datasets, extensive experimental results demonstrate that our method performs favorably against state-of-the-art methods on ViT-based models.
Garment Metaverse: Parametric Digital Human and Dynamic Scene Try-on

As a new concept, the metaverse has been widely concerned by the industry, academia, media and the public. Many domestic and foreign companies have also set up in the field of the metaverse. The traditional 2D and 3D virtual fitting has not achieved breakthrough and development because of the technical problems of authenticity and timeliness. With this in mind, we developed a virtual fitting system based on the metaverse community, which includes two modules: parameterized virtual digital human modeling and multi-scene and multi-action fitting. The system realizes the construction of individualized virtual digital person. As the medium between the metaverse clothing community and the real world, it is used to achieve multi-category dynamic fitting display actions in the meta-universe clothing community platform for multi-category scenes. The system integrates the high simulation and synchronization of the metaverse with the virtual fitting system, to break the barriers of traditional virtual fitting technology and realize the combination of the garment industry and the metaverse. The experimental results show that the system can realize the construction of virtual digital human in 2.1ms at the fastest and realize the dynamic display of multi-action and multi-scene fitting.
Speech image data mining algorithm based on multimodal decision fusion

This paper proposes a data mining algorithm based on multimodal decision fusion, which is mainly used to solve the correlation relationship of multi-level and multi-level multimodal data, the algorithm combines the methods of statistics, queueing study, machine learning and Bayesian decision fusion, compared with the results obtained by single modality, single data and single method, the algorithm proposed in this paper retains the information contained in the data to the maximum extent, and the algorithm is applied to the analysis of both numerical and text-based data. The proposed algorithm can be further extended by modifying the data types and methods to form new methods.
Improved Convolutional Neural Networks by Integrating High-frequency Information for Image Classification

Deep convolutional neural networks are powerful and popular tools as deep learning emerges in recent years for image classification in computer vision. However, it is difficult to learn convolutional filters from the examples. The innate frequency property of the data has not been well considered. To address this problem, we find high-frequency information import within deep networks and therefore propose our high-pass attention method (HPA) to help the learning process. HPA explicitly generates high-frequency information via a stage-wise high-pass filter to alleviate the burden of learning such information. Strengthened by channel attention on the concatenated features, our method demonstrates consistent improvements upon ResNet-18/ResNet-50 by 1.36%/1.60% and 1.47%/1.39% on the ImageNet-1K dataset and the Food-101 dataset, respectively, as well as the effectiveness over a variety of modules.
Genetic algorithm in hopfield neural network with probabilistic 2 satisfiability

Genetic Algorithm (GA) is to convert the problem-solving process into a process similar to the chromosomal changes in biological evolution using the mathematical method and computer simulation operation. This meta-heuristic algorithm has been successfully applied to system logic and non-system logic programming. In this study, we will explore the role of the Bipolar Genetic Algorithm (GA) in enhancing the learning process of the Hopfield neural network based on the previous study of PRO2SAT, and generate global solutions of the Probabilistic 2 Satisfiability model. The main purpose of the learning phase of the PRO2SAT model is to obtain consistent interpretations and calculate the optimal prominence weights, and the GA algorithm is introduced to improve the ability of PRO2SAT to obtain consistent interpretation using its selection, crossover, and mutation operators, and thus to improve the ability of the logic programming model to get a global solution. In the experimental phase, simulation data are used for result testing, and three performance metrics are used to test the consistency interpretation and global solution acquisition ability of the proposed model, including mean absolute error, logic formula satisfaction ratio, and global minimum ratio. Experimental results show that GA, as a meta-heuristic algorithm, has better searching ability for optimal solution and can effectively assist logic programming.