Reviewerly/exHarmony huggingface dataset:
Preparation and Simulation of Quantum States of Matrices

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile.
Human Action Recognition Based on Graph Convolution Spatio-temporal Fusion Attention Mechanism

Human skeleton maps are concise and robust, and the recent advances in human action recognition, driven by graph convolutional networks (GCNs), are huge. At present, most human action recognition based on graph convolutional network, such as spatiotemporal graph convolutional network (ST-GCN), extracts the spatial features of skeleton points and the temporal features of consecutive frames. Inspired by this, considering the continuity of human actions during motion, the information of each action is related to the information of the previous time point, and a spatio-temporal fusion attention graph convolutional network is proposed for human action recognition. Spatially, the feature difference between the skeleton points of the previous and previous frames is used to generate an attention mask, and the spatial features of each frame are extracted in the temporal domain to generate an attention tensor. Compared with other methods such as ST-GCN, the comparative experiments designed to evaluate and test the model with CS and CV data sets on the data set NTU-RGBD verify the accuracy and effectiveness of the network model.
Fast CU Partition Algorithm Based on CNN in Intra Prediction

The latest video coding standard, Versatile Video Coding (VVC) also called H.266, adopts quadtree with nested multitype tree (QTMT) coding block structure and still uses recursive rate-distortion (RD) to find the best intraframe coding unit (CU) partition, which accounts for more than 97% of the coding time. To address this problem, in this paper, we propose a fast CNN-based intraframe CU partition algorithm. The proposed approach, containing the CNN model, is implemented on the VVC official software VTM 14.0. A CU size of 64 × 64 is the input of the CNN, and the common test condition (CTC) sequences are encoded using four QPs {22,27,32,37} in the All-Intra (AI) configuration to verify the proposed method. The results showed that the encoding time was reduced by 42.15% in AI mode at the cost of a 0.85% increase in Bjøntegaard delta bit rate (BD-BR) compared with the official VTM14.0.
High-density Image Object Counting Network Based on Spatial Context and Channel Attention

Accurate object counting is a challenging task in image analysis, low-density image object counting can usually be achieved by object detection algorithms, and high-density object counting still has limited counting accuracy. We propose a high-density image object counting network based on spatial context and channel attention, abbreviated as HIOC-Net, which divides low-level features into multiple blocks of different scales through a spatial context-aware module to extract rich contextual features, and then uses the channel attention-aware module to process the interdependence of feature information in the channel dimension, so that the model can focus on useful features, suppressing the irrelevant background. This paper conducts extensive experiments on large-scale crowd and vehicle counting datasets, including ShanghaiTech, UCF_CC_50, WorldExpo'10, TRANCOS, and HBR_YD datasets. The results show that our method not only surpasses many state-of-the-art methods in counting accuracy but also achieves competitive localization accuracy, resulting in high-quality object density maps.
Research on vehicle Path Tracking Control based on MPC

Aiming at the problem of path tracking of unmanned vehicles, this paper built a vehicle kinematics model and carried out discretization, designed a vehicle lateral motion controller based on model predict control (MPC) to track the vehicle's path on the basis of the existing path, and carried out controller simulation through MATLAB. Simulation results show that the controller has a certain path tracking accuracy.
Fast Pedestrian Detection Algorithm Based on Improved YOLOv3

Aiming at the problems of fast-moving speed, easy occlusion, and complex background of pedestrians in traffic scenes, a fast pedestrian detection algorithm based on improved YOLOv3 is proposed. First, choose the efficient lightweight network ShuffleNetv2 to replace the original backbone network Darknet-53 to reduce the model complexity and improve the detection speed. Second, a reverse residual structure is introduced in the detection network layer to enhance the expressiveness of features. Third, a coordinate attention mechanism is introduced to suppress useless information and enhance the network's ability to focus on key features. Fourth, the spatial pyramid pooling structure is introduced to realize multi-scale feature fusion of the network and improve the detection accuracy of small objects. The experimental results show that compared with YOLOv3, the improved YOLOv3 proposed in this paper can improve the detection accuracy and detection speed by 0.7% and 53.8% respectively, which is more conducive to the rapid detection of pedestrians.
Heterogeneous Graph Neural Network for Chinese Financial Event Extraction

Financial event extraction aims to detect events from financial announcements and extract corresponding event arguments. This task is challenging because financial announcements are often long text, the arguments of an event are always scattered among different sentences in the document, and multiple events can coexist in the same document. It requires a comprehensive understanding of the document and the ability to aggregate arguments across multiple sentences. Most existing sentence-level event extraction methods only extract event arguments within the sentence range. These methods are not very effective for this task, and it is difficult to handle a large number of financial announcements. To address these issues, we propose a novel heterogeneous graph-based model HGCFEE with six types of edges designed to capture the interactions between sentences and entities using heterogeneous graphs. In-depth experiments and comprehensive analysis demonstrate the superiority of HGCFEE over baseline methods.
A Deep Learning-based Recognition Model for Chinese Book Subject Words

In order to effectively identify subject words in Chinese books and documents, this paper proposes an automatic recognition model for subject words based on deep learning. The model first builds word vectors with the word to vector (Word2vec) model to obtain the feature information at the semantic granularity level of the vocabulary, and then uses the deep neural network (DNN) model to train the feature weights of the vocabulary to predict the probability that the keywords belong to the subject words to achieve binary classification. Finally, experimental results on a library bibliographic data set show that the TopicDNN model has a prediction accuracy of 85.32%, which has better performance for subject words recognition than traditional machine learning methods.
Research on Data Augmentation Strategy Methods for Image Caption

Data augmentation can effectively expand the number of samples in a dataset and increase the diversity of samples. Image caption refers to the generation of a description statement corresponding to an image, and its accuracy directly affects the accuracy of the description statement. In this paper, we study and analyze data augmentation and VizWiz dataset, then we find that data augmentation can effectively simulate the image quality problems existing in VizWiz dataset. In order to improve the accuracy of the image caption model on the VizWiz dataset, this paper presents a method based on a data augmentation strategy, which mainly uses four data augmentation operators to simulate camera shake, out-of-focus, flash and low light conditions. The strategy space also contains basic translate, shear and contrast operations for the image. The method achieves a score: BLEU_1 of 62.5, BLEU_4 of 23.1, ROUGE_L of 46.6 and CIDEr of 49.6 on the VizWiz dataset.
Robust Fuzzy Between-cluster Separation Clustering Based on Shrunk Patterns

The partition-based fuzzy c-means clustering (FCM) is widely used in machine learning and pattern recognition. FCM uses membership degrees to describe the fuzzy logic between samples and clusters. FCM obeys the prior model assumption of identical distribution and the distribution structure of the data affects the clustering results. However, noise and outliers will destroy the data structure and lead to poor clustering results. Aiming at these problems, a fuzzy between-cluster separation clustering algorithm based on shrunk patterns is proposed in this paper. Initially, the flexible manifold of original data is recovered using pattern shrinking. Compared with the original data space, the flexible manifold provides better denoising performance. Then fuzzy clustering is performed on the shrunk patterns. But the shrinking process overemphasizes the cohesion within clusters and tends to lose the information between clusters. Considering the between-cluster information, a fuzzy between-cluster measure is introduced to keep the separation of clusters in clustering the shrunk patterns, and two adaptive update rules are proposed to determine the parameters. Extensive experiments are carried out on several benchmark data sets. Experimental results show that the proposed algorithm is more robust and effective than FCM and its variants.
Chinese News Headline Classification Model Based on ERNIE and Deep Learning Algorithm

Although the Word2Vec model can solve the problem of sparse features and high dimensionality in text representation, it cannot handle the problem of multiple meaning words in Chinese vocabulary. Therefore, this paper proposes a text classification model (EBGM) based on a combination of ERNIE, bidirectional gated recurrent unit (BiGRU) and maximum pool processing. First, more contextual semantic representation of Chinese text is performed by ERNIE pre-training model; then, to enhance the relevance of contextual semantics, contextual semantic information is extracted by BiGRU; finally, maximum pooling is performed to obtain important information of the text. The final results on the experimental dataset show that the model has good performance in the Chinese news headline classification task, which proves the feasibility of the model.
Automatic Scene Segmentation Algorithm for Image Color Restoration

In the process of the automatic coloring of black and white movies, due to the issue of inaccurate scene segmentation, the selected reference image coloring is not suitable for two scenes, which further leads to poor coloring effects. Towards the issue, this paper designs an automatic scene segmentation algorithm based on deep learning, which can combine depth features and semantic similarities and not use the color distribution in the video. More specifically, the method first employs the pre-trained model VGG19 to learn the multi-layer feature representation of adjacent two frames. Secondly, the residual network is adopted to combine the multi-layer feature representations produced by the pre-trained model VGG19 to form feature vectors. Finally, the paper calculates the semantic similarity between the two feature vectors and designs an adaptive threshold scheme for determining the boundary frames, which can perform well in the scene segmentation task for various categories of videos. Experimental results show that this paper can effectively address the scene segmentation issue in various movies and thus improve the coloring effect of the ones.
Data-missing k-means based on intra-cluster and inter-cluster distances

This paper proposes a method that reduces the intra-cluster distance and increases the inter-cluster distance in the k-means problem with missing data. Filling in missing data, calculating intra-cluster distances between clusters, and clustering problems are integrated into one function, and solved through loop iterations. Finally, the method is applied to 4 UCI datasets, and the results show that the method has good effect.
Industry Classification Algorithm Based on Improved BERT Model

With the rapid development of the economy, many enterprises are derived, and different enterprises have different economic activities, and the description of economic activities is usually in the form of short texts. The latest National Standard of the People's Republic of China - Classification of National Economic Industries is divided into 1,381 categories according to categories, major categories, medium categories and minor categories, and industry classification often relies on human experience, which takes a long time and the work is more mechanical. This paper aims to reduce the time and labor costs consumed by traditional industry classification methods. This paper draws on short text classification algorithm, convolutional neural network algorithm, and considers the particularity of different industries. An industry classification algorithm based on the improved BERT model is proposed. Based on the BERT model, the algorithm combines the convolutional neural network and the three-channel model to analyze the main business description of the listed company from the three levels of words, words and concepts, so as to determine the industry attribution. Taking the main products of listed companies screened in Shanghai Stock Exchange and Oriental Fortune Net in the past three years as a data set, the experimental results show that this method is excellent for industry classification.
A regression test case generation method for avionics software

With the continuous development of the aviation field, the avionics requirement is also changing, so it is also necessary to take appropriate methods to test the changing avionics requirement to ensure aviation safety. This paper presents a regression test case generation method for avionics software, which can identify the changes in requirement model from the perspective of model, and generate regression test cases according to the coverage criteria in the DO-178C standard. Regression test cases consist of baseline test cases and supplementary test cases. We introduced how to extract reusable test cases from baseline test cases and supplement test cases that do not meet the coverage requirements. Finally, a real avionics requirement proves the effectiveness of our method.
Multi-loss joint cross-modal pedestrian re-identification method fused with grayscale and RGA

Cross-modal pedestrian re-identification is to match the color image and infrared image of the pedestrian to determine whether there is the same pedestrian. The images have large inter-modal differences and large intra-modal variations, resulting in low recognition accuracy. This paper proposes a multi-loss joint cross-modal pedestrian re-identification method that fuses grayscale images and relation-aware global attention (RGA). First, grayscale the color images in the cross-modal dataset to reduce the network's dependence on color information; second, use the trained generative adversarial network to convert the color images and infrared images in the dataset to each other to reduce pedestrian poses and modality changes; then, the RGA is embedded into a shared-weight dual-stream ResNet50 to capture more robust features; finally, the hard sample triplet loss is improved and converted into a cross-modal hard sample triplet loss. The cross-entropy loss of the smooth label is combined with the hard sample triplet loss and the cross-modal hard sample triplet loss, respectively, to form joint functions L1 and L2, and then supervised training of the network. Experiments are carried out on the RegDB and SYSU-MM01 datasets, and the mAP reaches 75.85% and 69.56%, respectively, which are better than many current methods, indicating that the proposed method has better recognition accuracy.
Time Interest Network for Click-Through Rate Prediction

In the era of the information explosion, it is very important to accurately predict the user's next behavior (such as browsing, collecting and commenting) through the user's characteristics. As a key research issue in the traffic distribution process of the Internet industry, CTR is of great significance to content recommendation and online advertising. However, most existing research ignores the intrinsic structure of user behavior traits: User behavior changes over time, leading to changes in user interest characteristics. Toward that, this paper proposes a new CTR model called click-through rate prediction (TIN) based on the network of temporal interest. The model first maps large-scale sparse input features into low-dimensional embedding vectors, the characteristics of the user's interest in each time window are then extracted by the multi-head self-attention mechanism, Bi-LSTM is then utilized to capture changes of interest between various time periods, and then, it is beneficial for the residual network to achieve gradient multiplication and backpropagation process, effectively avoiding the problem of gradient disappearance, and finally connecting to the multilayer perceptron (MLP) to learn the nonlinear relationship between features. Extensive experiments have conducted on the advertising dataset and the results have demonstrated that the proposed model is competitive with superior CTR estimation ability.
Super-resolution adversarial generative fish classification based on Swin-Transformer

The development of fish detection and identification technology is crucial in fishery production. Due to the large intra-class difference and high inter-class similarity, fish classification tasks are considered to be fine-grained. In addition, the acquisition of fish images is often affected by lumps, angles, complex backgrounds, weather, and other factors, which makes the classification task difficult. We present a fish classification method that combines super-resolution reconstruction, multi-scale feature information, and self-attention to improve image quality and classification. Through super-resolution reconstruction and data augmentation, image quality can be improved. A feature extraction network based on Swin-Transformer is used to extract the image's multi-scale feature information and self-attention. The method was evaluated using the Nature Conservancy's publicly available fisheries monitoring dataset. The results of the experiments reveal that the approach may significantly increase image quality while completing the fish classification. At the same time, we compared our results to those of other approaches.
Laplacian Pyramid Network for Transferring Picture into Van Gogh's Style

Artistic style transfer has become a heated field for machine learning research, where many methods exist to solve the problem. However, transferring a picture into another style is still a complicated image processing task. Each method has its advantages and disadvantages. Laplacian Pyramid Network (Lapstyle), is a novel feed-forward method that could provide artistic style transfer with high efficiency. In this paper, several of Van Gogh's paintings and various pictures are set as style images and content images, including portraits, scenery, and so on, to look at their performance in different situations. The result is that Lapstyle performs well when the style image is a distant view with simple color. At the same time, the stylized picture is awful when the style image is a portrait. Even though the style is paintings of the same artist, the performance varies significantly. In conclusion, the style image for Lapstyle is paintings with simple colors and few details, and portraits are unsuitable.
Deep Neural Network Based on Convolution Factor Decomposition for Wireless Signal Modulation Recognition

In recent years, deep neural network (DNN) technology is widely used in the radio signal modulation recognition task to achieve a high recognition accuracy. However, the performance of DNN depends on a large number of training samples to solve a large number of neural network model parameters. Thus, sufficient training data are required and solving DNN parameters is time-consuming. To alleviate these issues, we propose to use a special module - the Inception module combined with CNN to build a novel neural network for radio signal modulation recognition. In our approach, using the idea of convolution factorization (a key idea in the Inception module), we broaden the network to ensure the model learning ability while reducing the model parameters. Moreover, we use the data enhancement strategy to increase the number of training samples to improve recognition performance. The experiment results show the effectiveness of our approach.
Platform of Formal Modeling and Analysis for Airborne Software Requirements

Airborne software systems play very important roles in modern civil aircraft systems, and there are several safety standards, including DO-178B/C, etc., that are compulsory to be satisfied before airborne software can be certificated by the authority of government. According to the DO-178B/C, the consistency and integrity of airborne software requirements must be analyzed and verified in the early stage of software development. In this paper, we introduce a formal modeling and analysis tool platform (ART: Avionics Requirement Tools) for airborne software natural language requirements, and a case study of the requirements of the software subsystem of the Indication-Recording System (IRS) is provided. Firstly, we give the semantics of a formal Variable Relationship Model (VRM), the platform architecture, and toolchain of ART. Then a methodology of formal analysis of requirement consistency and integrity based on a multi-paradigm is given. After that, some details of the case study of IRS are shown including: how to make a preproccessing of original requirements and the automatic analysis process of the requirement model, such as the preprocessing and standardization of original requirement items, automatic generation of VRM models and multi-paradigm based formal analysis, etc. Lastly, some experiences of this case study are shown.