Reviewerly/exHarmony huggingface dataset:
Fuzzy C-Mean Clustering Algorithm Combining Inter-Cluster Distance

With the rapid and vigorous development of fuzzy clustering theory and methods, more fuzzy clustering algorithms have been proposed to establish the uncertainty description of the samples. However, when clustering is performed, existing fuzzy clustering algorithms mostly iterate feature weights or deal with noise.The objective function is mostly based on minimizing the Euclidean distance within the clusters. However, increasing the Euclidean distance between cluster centroids may also lead to an improvement in clustering performance.In this paper, a new fuzzy c-mean clustering algorithm (JCFCM) combining inter-cluster distances is proposed. Not only is an affiliation assigned within the original cluster, but it is also reflected in the form of affiliation between clusters.In this paper, clustering is performed by increasing the process of iterative selection of cluster centers between clusters. With this formalization an objective function is designed and the iterative formulas for the parameters in the function are obtained by solving the objective function optimally. Finally, experiments are conducted on five real data sets and compared with other fuzzy clustering algorithms. Overall, the JCFCM algorithm has better clustering results than the fuzzy C-mean algorithm and has some advantages over the existing improved fuzzy C-mean algorithm for different data sets.
IDSCAN:Image Dehazing Using Spatial and Channel Aware Network

Dehazing refers to a method that aims to remove the interference of haze in the image to obtain a high-quality image by some certain ways such as statistical knowledge, image restoration knowledge and deep learning knowledge. Some classical methods have been proposed for removing the haze and achieved some most pleasant performance. However, there is some aliasing phenomena in dehazing results. To address this issue, we propose an effective image dehazing using spatial and channel aware network(IDSCAN) to learn some features with strong representation ability from the images with free-haze. For spatial aware, we extract them by combining some convolutional information with some simple operations such as unfold and reshape. For channel aware, we compute the weight of each channel by the compression in the frequency domain which is implemented by the discrete cosine transform block network (DCTB). Extensive experimental results on the RESIDE haze dataset show that our method outperforms other state-of-art dehazing methods in terms of qualitative and quantitative methods. Simultaneously, we also effective improve the aliasing phenomena of images removed the haze.
Quantum Embeddings of Classical Data for Quantum Machine Learning

A major area of research in the field of quantum machine learning is the analysis of the loss landscape, particularly of variational quantum algorithms. These works often provide bounds and generalizations for various ansatzes and quantum embedding strategies. These analyses include approaches such as the Hessian and Fisher information matrices as well as generalized trigonometric polynomials. However, many such reviews often rely on a rotational encoding in practice or focus on few different approaches. The goal of this work is to statistically analyze experimental results from a quantum machine learning model that employs various different quantum embedding approaches, including those covered in related work, as well as the effect of measurement basis on the model.
Causal Discovery Based on Hybrid Structural Equation Model

Causal relation is the cornerstone of human understanding and exploration of the world. Inferring causal relations between things has been of interest to researchers. Most traditional methods are designed purely for discrete or continuous data, yet mixed data are widely available. This paper proposes a causal discovery method based on a hybrid structural equation model. The main idea is to formulate a nonlinear causal mechanism for mixed data through a hybrid structural equation model, while incorporating the ideas of structural equation and probabilistic noise in likelihood maximization, which realizes efficient causal inference on mixed data. Experimental results on synthetic and real-world datasets show that the method improves the accuracy of causal inference for mixed data and it’s robust to anomalous data.
An Automatic Medical Image Segmentation Approach via Dual-Branch Network

Medical image segmentation is a basal and essential task for computer-aided diagnosis and quantification of diseases. However, robust and precise medical image segmentation is still a challenging task on account of much factors, such as complex backgrounds, overlapping structures, high variation of appearances and low contrast. Recently, with the strong support of deep convolutional neural networks (DCNNs), the encoder-decoder based segmentation networks have been the popular detection schemes for medical image analysis, yet image segmentation based on DCNNs still faces some limitations, such as restricted receptive field, limited information flow, etc. To address such challenges, a novel dual-branch deep residual U-Net network is proposed in this paper for medical image detection which provides more avenues for information flow to gather both high-level and low-level feature maps and a greater depth of contextual data.A residual U-Net network is constructed for efficient feature expression using residual learning, attention block, and feature expression. Meanwhile, fused with atrous spatial pyramid pooling (ASPP) block and squeeze-and-excitation (SE) block, The residual U-Net network is suggested to embed an attention fusion block to gather multi-scale contextual data. On the basis, To fully utilize local contextual data and increase segmentation precision, a dual-branch deep residual U-Net network is built by stacking two residual U-Net networks. Combined with multiple public benchmark data sets on medical images, including the CVC-ClinicDB, the GIAS set and LUNA16 set, experimental results indicate the superior ability of proposed segmentation network on medical image segmentation compared with other advanced segmentation models.
Association Estimation of English Education Level Using Artificial Neural Network Algorithm

In order to improve the accuracy of English education level evaluation, this paper puts forward a design method of associated estimation model of English education level based on artificial neural network. Establish a multiattribute decision-making constraint parameter model for the correlation assessment of English education level, and analyze the multi-attribute decision-making and quantitative characteristics of the correlation assessment of English education level combined with the multi-dimensional explanatory variable and control variable parameter identification methods. Combined with the artificial neural network modeling method, the feature clustering analysis of the English education level is carried out; the adaptive learning and training method of the artificial neural network is used to establish the attribute fusion set and the semantic ontology feature distribution set of the multi-attribute decision-making for the correlation evaluation of the English education level; using the artificial neural network The network output layer fusion control method realizes the optimization of the multiattribute decision-making process. The simulation results show that the method has a good effect on the intelligent decisionmaking of the correlation evaluation of English education level, and improves the accuracy of the evaluation results of English education level.
GCN-Based Pollution Source Screening Algorithm and Optimization for Underground Drainage Pipe Network

Source tracing is an important solution to address pollution in underground drainage networks. However, due to the complex and variable hydrological conditions of underground drainage networks, it is still very difficult to accurately trace the pollution sources of underground drainage networks. A graph convolutional neural network can effectively extract structural information from graph data. In this paper, the graph convolutional neural network is used for pollution tracing of underground drainage networks, achieving high accuracy in determining the number of pollution sources and the location of pollution sources.
A Model of Spoken Language Understanding Combining with Multi-Head Self-Attention

Spoken Language Understanding (SLU) is a very important module in intelligent dialogue systems. It is usually constructed based on a bi-directional long and short-term memory network (BiLSTM). It has some shortcomings, such as relative single representation of feature space and fuzzy semantic features. For this reason, this study constructs a SLU model which combines the temporal characteristics of context and the characteristics of multi-layer representation space. The model combines a bi-directional long and short-term memory network and a multi-head self-attention to extract different feature information of contextual temporal features and multisemantic representation space of the text, respectively; then, the two features are fused using a residual linking method to enhance the features of word dependence at different locations within the text; meanwhile, the gate mechanism is then used to enable the intent detection task to establish an influence relationship on the slot filling task. Finally, the SNIPS dataset, the ATIS dataset, and the slot-gated model are selected for comparison experiments. The slot filling F1 value is increased by 4.14% and 1.1% respectively, and the accuracy of semantic framework is increased by 4.25% and 2.50% respectively. The results show the effectiveness of the model of SLU task.
Tiny Object Detection with Lightweight YOLOv5 Based on Attention Mechanism

In object detection tasks, Frontier small object detection algorithms basically suffer from low accuracy in detecting small objects. Given this situation, we propose the lightweight YOLOv5 small object detection algorithm it is an attention mechanism based algorithm. Using the depth-separable convolution criterion of the original model to realize the lightweight of the model, we modify the neck of YOLOv5 and combine FPN and PANet to fuse different semantic information in four feature maps, thus improving the quality of feature extraction and the performance of small object detection. We modified the main feature extraction network of YOLOv5. Based on the original output of three feature maps, Incorporate new feature images so as to achieve feature enhancement of the initial image. We replace the original ReLU activation with Swish activation using an activation function that is more suitable for retaining the target function. To enrich the target detection environment, we applied the data augmentation method, Here we used a dynamic update of parameters using cosine annealing for training of parameters and ported it to our newly investigated YOLOV5 algorithm. And it was validated effectively and allowed to experiment on the YOLO family of algorithms in the CityPrersons dataset. The results show that our innovative YOLOv5 algorithm can be very effective for the detection of small objects.
Fine-Grained Complex Image Classification Method Based on Butterfly Images

Fine-grained image classification is very difficult task and there is currently no effective machine learning method yet. Taking the butterfly images as an example, this paper comprehensively studies image classification methods for finegrained and complex images, focusing on improving the existing methods in the four aspects of image preprocessing, feature extraction, feature coding, and classifier design. An effective machine learning method for butterfly classification is established. In the aspect of image preprocessing, we firstly introduce the edge breakpoint connection method to make up for the defect that discontinuous edge can’t extract the target region effectively. In terms of feature extraction, nonseparable Shannon wavelet is used to combine with corner feature, blob feature, edge curvature feature and invariant moment feature selection to further improve the utilization of image information. In the aspect of feature encoding, most features in this paper are encoded by histogram, which increases the classification efficiency, and for histogram encoding features, an improved histogram intersection distance is proposed, which makes it more effective in KNN classifier. Finally, in the classifier design, the Bagging ensemble method is integrated into the parallel KNN classifier. Experiments show the effectiveness and robustness of the proposed method.
Income Prediction of Urban and Rural Residents Based on BP in Digital Economy

In order to formulate more reasonable economic policies to improve people’s material living standards, an income prediction model of urban and rural residents based on back propagation (BP) neural network was proposed, so as to better understand the income situation of urban and rural residents. Particle swarm algorithm (PSO) was used to optimize the BP neural network. The experimental results showed that optimizing BP neural network by PSO algorithm can get better neural network weights, so as to further reduce the output error of the BP neural network. Using the prediction model based on PSO-BP can accurately predict the income of urban and rural residents, and the error between the predicted value and the actual value is less than 1.5%, which not only verify the necessity of optimal design, but also verify the feasibility of proposed prediction model, which has some practical reference value.
Evaluation of Rural Teachers’ Informatization Ability Based on Neural Network

Aiming at the objective demand of rural teachers’ informatization ability evaluation, a rural teachers’ informatization ability evaluation model based on BP neural network is proposed. Among them, the design ability and software index are taken as the input of BP neural network, and the evaluation results of rural informatization ability are output through the training of BP neural network. The results show that after 24 iterations of training, BP neural network can meet the requirements. In the comparison of the prediction results of informatization capability evaluation, the correlation coefficient between the predicted value and the actual value obtained in this study is 0.90173, which proves the credibility of the evaluation prediction results. It shows that BP neural network can be used for the evaluation of rural teachers’ informatization ability.
Object Tracking Method Combined with Lightweight Hybrid Attention Siamese Network

Aiming at the problem that the target tracking method of deep learning has a large number of model parameters and insufficient real-time performance, it is difficult to apply to mobile terminals or embedded devices with insufficient computing power. A lightweight hybrid attention-based twin network tracking algorithm is proposed. Firstly, based on MobileNetv3-Large network, group convolution and channel rearrangement are performed; then, in view of the problem that traditional attention mechanism only considers a single scope, this paper proposes a lightweight group-gated mixed attention (Group-gated mixed attention, GG); finally, GG is embedded in the Siamese network structure of this paper and the hierarchical feature fusion strategy is used to improve the tracking accuracy. Experiments show that the parameters of the proposed GG decrease by 26.2% compared with CBAM, decrease by 6.50% compared with SE, and increase Top-1 by 2.59% and 2.68% respectively; the experiments on the OTB100 and VOT2018 datasets demonstrate that the proposed algorithm is comparable to traditional tracking Compared with the algorithm, the accuracy and real-time performance have great advantages.
Text Detection and Recognition in Natural Scene with Key-Points Detection and Multi-Hot Structure

This paper present a unified network architecture for natural scene text detection and recognition. The proposed method uses key-points to locate each character. By predicting the geometric centers of text instances, the start and end points are obtained by regression, and the subsequent one of each points in the text instances are calculated using the nearest distance. A multi-hot structure is adopted in the recognition branch to predict the character corresponding to each key-point, greatly reducing resource consumption. The proposed network structure is simple, easy to implement and surpasses many previous state-of-the-art works for end-to-end recognition tasks.
Multifactorial Ant Lion Optimisation Algorithm

Evolutionary multitasking is a new research direction proposed in 2016. It is designed to simultaneously improve the convergence properties of multiple optimisation problems by seamlessly transferring knowledge between them. As an effective realization paradigm of evolutionary multitasking, multifactorial optimisation (MFO) has received extensive attention since it was proposed. Here, to further explore the generality of MFO, we employ an emerging optimisation algorithm to implement the MFO paradigm. In this work, for the first time, we use the ant lion optimisation algorithm as a search mechanism to achieve multifactorial optimisation and propose a multifactorial ant lion optimisation (MFALO) algorithm. The main purpose is to further improve its optimisation performance by realizing the multi-tasking processing of ant lion optimisation so as to obtain better convergence speed and accuracy. We compare the proposed algorithm with the base algorithms on various benchmark problems. Experimental results show that multifactorial ant lion optimisation can effectively improve the accuracy of benchmark problems through an effective assortative mating design.
News Text Classification Method for Edge Computing Based on BiLSTM-Attention

In the coming smart city, the explosive growth of data makes the amount of data contained in news texts more and more, which leads to the decrease in the accuracy of traditional machine learning or deep learning models in the news text classification. Therefore, in this paper, we propose a news text classification model based on BiLSTM-Attention. The data set is selected as 30,000 news texts, and the word segmentation is carried out in turn. The stop words are removed, and the word vector is quantified. Then, the data set is cross-validated according to the ratio of training set to validation set of 8:1. Finally, the experiments with the bilstm model, lstm model and bilstm-short text model show that the BiLSTM-Attention model has the highest accuracy and the lowest loss value. In order to further verify the classification performance of BiLSTMAttention model, the experiment is designed again and Bayes and SVM are added to compare. The experimental results show that the accuracy, recall and F1 value of BiLSTM-Attention model are the highest, which proves that BiLSTM-Attention is more suitable for news text classification.
Evaluating Effectiveness of Using Multi-Features to Differentiate Real from Fake Facial Images

Face analysis is one of the key research areas in the field of computer vision with applications in numerous areas. Face recognition, emotion recognition, and more recently deepfake detection have greatly benefited from the advancements in the field of face analysis. Our research attempts to identify useful facial features for analysis. We first analyze the effectiveness of geometric facial features for the purpose of emotion recognition. In later experiments, a fusion scheme was created based on the preliminary analysis,which tested the performance of these selected features for the identification of real and fake images. We include local image features in combination with geometric facial features to measure their effectiveness in fake image detection tasks. The promising results produced in this study can be used to perform a more in-depth analysis of face geometry and its result in facial analysis.
Adversarial Meta Learning Improves Low-Resource Speech Recognition

Low-resource automatic speech recognition is a chal- lenging task. To solve this issue, multilingual meta-learning learns a better model initialization from many source language tasks, allowing for rapid adaption to the target language. However, due to the lack of limitations on multilingual pre-training, the shared semantic space of different languages is difficult to learn. In this work, we propose an adversarial meta-learning training approach to solve this problem. By using the adversarial auxiliary aim of language identification in the meta-learning algorithm, it will guide the model encoder to generate language-independent embedding features, which can improve model generalization. And we use Wasserstein distance and temporal normalization to optimize our adversarial training, making the training more stable and easier. The approach is evaluated on the IARPA BABEL. The results reveal that our approach only requires half as many meta learning training epochs to attain comparable multilingual pre-training performance. It also outperforms the meta learning in all target languages fine-tuning and achieves comparable performance in small data scales. Specially, it can reduce CER from 71% to 62% with fine-tuning 25% of Vietnamese data. Finally, we show why our approach is superior than others by using t-SNE.
Recent Advances on Thermal Infrared Target Tracking: A Survey

Thermal infrared (TIR) target tracking task is not affected by illumination changes and can be tracked at night, on rainy days, foggy days, and other extreme weather, so it is widely used in night auxiliary driving, unmanned aerial vehicle reconnaissance, video surveillance, and other scenes. Thermal infrared target tracking task still faces many challenges, such as occlusion, deformation, similarity interference, etc. To solve the challenge in the TIR target tracking scenarios, a large number of TIR target tracking methods have appeared in recent years. The purpose of this paper is to give a comprehensive review and summary of the research status of thermal infrared target tracking methods. We first introduce some basic principles and representative work of the thermal infrared target tracking methods. And then, some benchmarks for performance testing of thermal infrared target tracking methods are introduced. Subsequently, we demonstrate the tracking results of several representative tracking methods on some benchmarks. Finally, the future research direction of thermal infrared target tracking is discussed.
Fatigue Driving Detection Based on Improved YOLOV5

Fatigue driving detection based on YOLOV5 object detection algorithm. YOLOV5N with fewer parameters is selected as the basic model, and the large object detection layer in YOLOV5N is removed according to the object size clustering results, which reduces the parameters and improves the detection results. SAM is introduced to improve the ability of the backbone network to extract key features, and the convolution kernel in SAM is expanded to provide a wider receptive field for the model, in exchange for better detection results with a small increase in parameters. Referring to BiFPN, the Neck part of YOLOV5N is modified to provide more diverse fusion methods for multi-scale features. The precision, recall and mAP of the improved model are higher than those of YOLOV5N.