Reviewerly/exHarmony huggingface dataset:
Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

Infinite Gaussian mixture process is a model that computes the Gaussian mixture parameters with order.This process is a probability density distribution with adequate training data that can converge to the input density curve.In this paper, we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling.A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling.The solution uses data mining techniques to link datum in groups by linking their components.The learning techniques are novel presentations of Gaussian modelling that use prior knowledge of the representation hyper-parameters and approximate them in a closed form.Experiments are performed on axial data modeling of Arabic Script classification and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Arabic patterns.Experiments are also performed on the application of facial expression recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.
An approach for credit scoring based on sequential pattern mining

Credit scoring is a significant research domain for both finance and computer science researchers. Customers’ loan application behavior is highly related to their future default performance, which requires more studies. This study applies a modified approach of sequential-pattern-mining-based classification to mine important and discriminant behavioral patterns of customers. And then, we use the patterns to predict customers’ likelihood to default on a future loan. Our approach specifically addresses the problem of data imbalance. Evaluation based on real business data shows that our approach outperforms a series of time series classification methods, including deep learning models. In addition, such pattern-based classification approaches have the merit of explainability in features. Specific patterns of customer loan applications are found to be related to their future default behavior. Therefore, our method enhances the business understanding, provides managerial insights, and thus is more likely to be accepted by the industry.
Message board information extraction based on LDA and RNN

The message board text put forward by netizens on a certain issue is a suggestion or opinion, which is sparse and emotional. The traditional LDA model cannot solve the sparsity problem of the short message and ignores the emotional factor. In order to solve the above problems, a message board information extraction method based on LDA model and RNN model is proposed. First, eigenvalues are introduced to classify the text to solve the sparsity problem based on LDA model. Second, RNN model is used to realize the emotional features on the basis of message text vectorization. The experiment shows that the LDA model with eigenvalues has better topic extraction ability compared with the traditional model, and with the fusion of the RNN model, it can comprehensively display the potential information of the message text. As a result, the proposed method achieves information extraction maximize.
Aspect-level text sentiment analysis method combining Bi-GRU and AlBERT

As more and more industries and enterprises begin to develop the online/offline business model, the demand for efficient sentiment analysis of online service user comments is increasing day by day. In view of the problem that the aspect emotion analysis model is difficult to combine the semantic information of the text effectively, and the accuracy of aspect emotion classification of the texts need to be improved, this paper uses GRU model to improve the ATAE-LSTM model and improves AlBERT model through the attention mechanism based on aspect level. The ATAE-AlBERT-BiGRU model is proposed. In this paper, comparative experiments are carried out on the SemEval 2014 dataset, and the results show that the accuracy of the proposed model is significantly improved compared with the comparison model in the aspect level emotion analysis task, and it performs well in reducing the computational load of the model.
Configurable image recognition framework design based on KNN and bit-based similarity model

CAPTCHAs are widely utilized on the Internet to partially protect against computer attacks, and text-based CAPTCHAs are commonly used. In order to make the more flexible attack, this paper provides a framework with configurable options based on k-NN, including three major parts: preprocessing the binary image, building standard library and recognizing image. The standard library is built from training dataset, where the third part can be an option to drop out some characters with a high similarity, and the library is used for testing dataset. A bit-based similarity model is proposed, where "and" and "or" bit operations are executed, and the result is the ratio of both operations. Finally, the framework is applied into four typical scenarios, MNIST handwriting database, CAPTCHAs built by the CAPTCHA generator, online CAPTCHAs of CNKI website, and CAPTCHAs within open source PHP DedeCMS, the average classification accuracy is 97.05%. As a result, the model is simple but effective, the framework can work well for text-based CAPTCHAs and handwritten numbers, which may make associated websites pay more attention to current authentication mechanism, and it offers flexibility to cover more algorithms and application scenarios by implementing different logics of preprocessing according to defined APIs.
Self-dual codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msub></mml:math> and s-extremal unimodular lattices

New s-extremal extremal unimodular lattices in dimensions 38, 40, 42 and 44 are constructed from self-dual codes over F5 by Construction A. In the process of constructing these codes, we obtain a self-dual [44,22,14] code over F5. In addition, the code implies a [43,22,13] code over F5. These codes have larger minimum weights than the previously known [44,22] codes and [43,22] codes, respectively.
Skeleton-based one-shot action recognition on graph neural network

In our work, we implemented one-shot action recognition that using the skeleton data. In terms of data preprocessing, we used the form of mapping skeleton sequence coordinates into signal images. In the feature extraction module, we used feature extraction based on resnet18. In the few-shot learning part, we adopted the metric neural network model based on graph neural network. Finally, the leading accuracy is realized on the ntu-rgbd120 one-shot dataset.
Blockchain Technology in Real-Time Governance: An Indian Scenario

Blockchain, the latest distributed ledger technology, has brought paradigmatic shift to the mode of transaction and communication recently. None of the arenas/sectors/organisations is untouched of the technology globally. Recently, the appliance of the distributed ledger technology into digital currency astonished the global community. The applications, apprehension, risks and prospective of technology, thus, have been analysed periodically. In this article, the authors try to understand the potential scope, complexity and legal dynamics of blockchain in the Indian scenario, intermittently. The basic sources such as reports and documents of NITI Aayog, Working Paper of Ministry of Information and Technology as well as reports of Finance Ministry are taken to start with. Moreover, guidelines and press-releases of the Reserve Bank of India, various judgements of the Indian Supreme Court and Ministers’ statements in the Indian Parliament and various initiatives of states have also been taken as references to critically comprehend the convolution of blockchain in the Indian context.
A 3DMM-Based Framework for Deformation Measurement in Face Rehabilitation

Reconstructing a 3D model of the face from a single 2D image is a long studied problem, but it remains challenging especially when capturing local and asymmetric deformations of the face is important. Computing a measure of such local deformations can find application in monitoring rehabilitation exercises in patients that are recovering from a stroke or in patient with Parkinson's and Alzheimer's disease. In this study, we present a complete framework for accurately deforming a 3D Morphable Shape Model (3DMM) of the face to a target RGB image. The used 3DMM is based on localized components of deformation, while the 3D to 2D fitting transformation is guided by the correspondence between landmarks detected in the target image and landmarks manually annotated on the average 3DMM. The fitting has also the peculiarity of being performed in two steps, disentangling face deformations that are due to the identity of the target subject from those induced by facial actions. In the experimental validation of the method, we used the MICC-3D dataset that includes 11 subjects each acquired in one neutral pose plus 18 facial actions that deform the face in localized and asymmetric ways. For each acquisition, we fit the 3DMM to an RGB frame with an apex facial action and to the neutral frame, and computed the extent of the deformation. Results indicated that the proposed approach can accurately capture the face deformation even for localized and asymmetric ones. Interestingly, these results were obtained just using RGB targets without the need for 3D scans captured with costly devices. This opens the way to the use of the proposed tool for remote medical monitoring of rehabilitation.
Occluded Facial Recognition for Surviellance Using Deep Learning

Nowadays, due to the advancement in technology, facial recognition is becoming one of the methods to identify a person. One of the challenges arises due to occlusion or partial covering of face, especially with a facial mask or a scarf. In this work, we use deep neural networks to solve the problem of recognizing such an occluded face. For this work, we have used three publicly available facial datasets, namely Labelled Face Wild dataset, COMASK20 and Specs on Faces (with images having low illumination), cumulatively consisting more than 5000 facial images. We evaluated four existing facial detection classifiers namely OpenCV, Single Shot Detection(SSD), Multi-task Cascaded Convolutional Neural Network(MTCNN) and RetinaFace. We found MTCNN to be most relevant for our work. We proposed a new Convolutional Neural Networks (CNN) as part of this work. We got accuracy of 99.38% for LFW, 99.62% for COMASK20 and 98.33% for SOF dataset.
Brain Inspired Keypoint Matching for 3D Scene Reconstruction

In this paper, we investigate the keypoint matching problem in a 3D scene reconstruction system. 3D scene reconstruction using a sequential set of images or video is an essential component in various virtual reality(VR) and augmented reality(AR) solutions. Keypoint matching is necessary for achieving a close to reality model of the scene using varying views. Although deep learning-based methods have been readily proposed for image matching using the keypoints and respective descriptors. These methods do not take into account the previous image matches when performing correspondence on the current pair of images. This is crucial in the presence of sequential images or frames from a video. A continual learning-based image matching framework is proposed that replicates the working of a human brain. The method efficiently extracts knowledge, stores the knowledge in its memory, and reuses it for future matches. The proposed method increases the expressiveness of the descriptors to be used for keypoint matching in the pair of images. Specifically, the methodology using a continual graph attention network to find the correspondence among keypoints in a pair of images. The methodology is thoroughly validated on a challenging benchmark dataset namely HPatches. The methodology is evaluated along with present state-of-the-art handcrafted and learning-based image matching methods under varying confidence thresholds. The experimental results reveal that the proposed methodology outperforms all the underlying methods while achieving significant improvement.
Multi-scale vision transformer classification model with self-supervised learning and dilated convolution

Benefiting from the advantages of good parallelism and features that support long-distance dependency modeling, a variety of ViT models based on the self-attention mechanism show outstanding performance in image classification tasks. However, these works have poor classification accuracy when trained on small datasets owing to insufficient attention toward local features. Therefore, this study presents a new self-supervised multi-scale ViT classification model , SMvT. This model adopts twin-tower architectures as the self-supervised framework and the hierarchical Swin Transformer as the backbone and proposes a MDP embedding layer to fully pay attention to local details. We investigated the model’s performance pretrained using the lightweight ImageNet dataset. Compared with recent self-supervised Transformers for vision, such as MoCo v3 and DINO, the classification accuracy of the SMvT has improved by up to 15.2%. SMvT combines self-supervised learning and depthwise separable dilated convolution, which is a lightweight and high generalization ViT model supporting cross-scale attention modeling. • Constructed a self-supervised vision Transformer classification model. • Proposed a multi-scale dilated pyramid (MDP) embedding layer. • Designed a depthwise separable dilated convolution (DSD conv).
Impacts of the COVID-19 lockdown measures on coarse and fine atmospheric aerosol particles (PM) in the city of Rome (Italy): compositional data analysis approach

In the year 2020, Italy faced a pandemic due to the virus SARS-CoV-2 for short COVID-19. Following this pandemic, a national lockdown period was imposed and throughout the year 2020 various measures were taken by the government to limit the mobility of people and contain the mortality associated with COVID-19. In Italy, pandemic measures led to a reduction in anthropogenic activities and provided an unprecedented opportunity to evaluate the possible effects that restrictions on anthropogenic activities may have on the air quality. Two background site (i.e., Cipro and Cinecittà) and a traffic sites (i.e., Corso Francia) were studied in the city of Rome. PM10 and PM2.5 were considered for the years 2019 and 2020. Moreover, the vehicular mobility, the emission classes of the vehicles, and the people mobility were taken into consideration along with meteorological variables. A compositional data analysis was used to evaluate the effect of pandemic measures on the fine- and coarse-size fractions of PM in the three considered sites. The results showed that in the traffic site (i.e., Corso Francia site) in 2020, there was a reduction of fine-size fraction of PM of about 10% when compared to the data of 2019, whereas in the background site (i.e., Cinecittà site) in 2020 there was an increase of fine-size fraction of PM of about 14% when compared to the data of 2019. No variation in the coarse- and fine-size fractions of PM were observed at the background site Cipro. This study showed how, in an urban context, PM can be influenced by strong changes in people's habits and in vehicular mobility such as those recorded during the investigated period and due to pandemic lockdown measures.
Performance analysis of quantum key distribution using polarized coherent-states in free-space channel

In free space channel, continuous-variable quantum key distribution (CV-QKD) using polarized coherent-states can not only make the signal state more stable and less susceptible to interference based on the polarization non-sensitive of the free-space channel, but also reduce the noise introduced by phase interference. However, arbitrary continuous modulation can not be carried out in the past polarization coding, resulting in that the signal state can not obtain arbitrary continuous value in Poincare space, and the security analysis of CV-QKD using polarized coherent-states in free space is not complete. Here we propose a new modulation method to extend the modulation range of signal states with an optical-fiber-based polarization controller. In particular, in terms of the main influence factors in the free-space channel, we utilize the beam extinction and elliptical model when considering the transmittance and adopt the formulation of secret key rate. In addition, the performance of the proposed scheme under foggy weather is also taken into consideration to reveal the influence of severe weather. Numerical simulation shows that the proposed scheme is seriously affected by attenuation under foggy weather. The protocol fails when visibility is less than 1 km. At the same time, the wavelength can affect the performance of the proposed scheme. Specifically, under foggy weather, the longer the wavelength, the smaller the attenuation coefficient, and the better the transmission performance. Our proposed scheme can expand the modulation range of signal state, and supplement the security research of the scheme in the free-space channel, thus can provide theoretical support for subsequent experiments.
Detecting and Classifying Nuclei Using Multi-Scale Fully Convolutional Network

The detection and classification of nuclei play an important role in the histopathological analysis. It aims to find out the distribution of nuclei in the histopathology images for the next step of analysis and research. However, it is very challenging to detect and localize nuclei in histopathology images because the size of nuclei accounts for only a few pixels in images, making it difficult to be detected. Most automatic detection machine learning algorithms use patches, which are small pieces of images including a single cell, as training data, and then apply a sliding window strategy to detect nuclei on histopathology images. These methods require preprocessing of data set, which is a very tedious work, and it is also difficult to localize the detected results on original images. Fully convolutional network-based deep learning methods are able to take images as raw inputs, and output results of corresponding size, which makes it well suited for nuclei detection and classification task. In this study, we propose a novel multi-scale fully convolution network, named Cell Fully Convolutional Network (CFCN), with dilated convolution for fine-grained nuclei classification and localization in histology images. We trained CFCN in a typical histology image data set, and the experimental results show that CFCN outperforms the other state-of-the-art nuclei classification models, and the F1 score reaches 0.750.
Stochastic cubic-regularized policy gradient method

Policy-based reinforcement learning methods have achieved great achievements in real-world decision-making problems. However, the theoretical understanding of policy-based methods is still limited. Specifically, existing works mainly focus on first-order stationary point policies (FOSPs); in some very special reinforcement learning settings (e.g., tabular case and function approximation with restricted parametric policy classes) some works consider globally optimal policy. It is well-known that FOSPs could be undesirable local optima or saddle points, and obtaining a global optimum is generally NP-hard. In this paper, we propose a policy gradient method that provably converges to second-order stationary point policies (SOSPs) for any differentiable policy classes. The proposed method is computationally efficient, and it judiciously uses cubic-regularized subroutines to escape saddle points while at the same time minimizing the Hessian-based computations. We prove that the method enjoys the sample complexity of O ˜ ( ϵ − 3 . 5 ) , which improves upon the current optimal complexity O ˜ ϵ − 4 . 5 . Finally, experimental results are provided to demonstrate the effectiveness of the method. • We propose a new algorithm SCR-PG which achieves the good properties of previous works. • We provide a non-asymptotic analysis of SCR-PG’s complexity with high probability. • Experimental results are presented to validate the superior performance of SCR-PG.
An English Diagnostic Intelligence Evaluation Model Based on Organizational Evolutionary Information Entropy

English is widely used as a universal language in the world, but there are still great limitations in the diagnosis and evaluation of English, which leads to serious errors in the actual use of English. In order to realize the English diagnostic intelligence evaluation, improve the English learning ability, and construct the cognitive framework of English learning, this study proposes an English diagnostic intelligence evaluation model based on the organizational evolution information entropy. Firstly, this study adopts the HRNet text feature extraction model and uses the pretrained model to extract text features from text. Secondly, this study adopts the organizational evolution algorithm to simulate the process of sorting, merging, and cooperating in the evolution of the population and classify and organize the characteristic population. Finally, the information entropy method is used to represent the amount of information contained in each feature for evaluation, and the optimal solution is found. The method proposed in this study has good experimental results in actual testing and comparison and can basically realize the function of English diagnostic intelligence evaluation.
Panet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting

Crowd counting aims to learn the crowd density distribution and estimate the number of objects (e.g. persons) in images. The perspective effect, which significantly influences the distribution of data points, plays an important role in crowd counting. In this paper, we propose a novel perspective-aware approach called PANet to address the perspective problem in crowd counting. Based on the observation that the size of the objects varies greatly in an image due to the perspective effect, we propose a dynamic receptive fields (DRF) framework. The framework is able to adjust the receptive field by the dilated convolution parameters according to the perspective property of different local areas in the input image, which helps the model to extract more discriminative features for each local region. Furthermore, different from most previous works that use Gaussian kernels to generate the density map as the supervision information, we propose a self-distilling supervision (SDS) training method. The ground truth density maps are refined from the first training stage and the perspective information is distilled to the model in the second stage. With combination of the proposed DRF and SDS, our model can adapt to perspective effect and different density maps to achieve better accuracy. The extensive experimental results on ShanghaiTech Part_A and Part_B, UCF_QNRF, and UCF_CC_50 datasets demonstrate that our proposed PANet outperforms the state-of-the-art methods by a large margin.