Reviewerly/exHarmony huggingface dataset:
Dilemma and Countermeasures of English Teaching Reform Based on Multiculturalism in the Context of Big Data and Wireless Communication

The development of big data and wireless communication technology has expanded the channels for Western culture to enter China. Diversified teaching forms make us dizzy, which has a great impact on traditional English teaching. This paper aims to study the dilemma and countermeasures of English teaching reform based on multiculturalism under the background of big data and wireless communication. It analyzes the connotation of multiculturalism, the characteristics of the era of big data, the problems existing in college English teaching, and the necessity of college English teaching reform. On the basis of feasibility, in order to understand the current situation of English teaching in colleges and universities, this paper takes teachers and students of a college in this province as the research object, conducts a questionnaire survey on them, and finds out how to better use the background of big data and wireless communication through data analysis. Combined with the multicultural trend of the times to carry out English teaching, the study concluded that under the current technological background, our English teaching must adhere to the student-centered approach and take the improvement of teachers’ quality as an important means.
Feature Extraction and Identification of Calligraphy Style Based on Dual Channel Convolution Network

To improve the effect of calligraphy style feature extraction and identification, this study proposes a calligraphy style feature extraction and identification technology based on two-channel convolutional neural network and constructs an intelligent calligraphy style feature extraction and identification system. Moreover, this paper improves the C3D network model and retains 2 fully connected layers. In addition, by extracting the outline skeleton and stroke features of calligraphy characters, this paper calculates the feature weight and authenticity determination function and constructs an authenticity identification system. The experimental study shows that the calligraphy style feature extraction and identification system based on the dual-channel convolutional neural network proposed in this paper has a good performance in calligraphy style feature extraction and identification.
PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION

Over a long time, atmospheric changes have been caused by natural phenomena. This study uses the Principal Component Analysis (PCA) model combined with Vector Autoregressive Integrated (VARI) called the PCA-VARI model through the data mining approach. PCA reduces ten variables of climate data into two principal components during ten years (2001-2020) of climate data from NASA Prediction Of Worldwide Energy Resources. VARI is a non-stationary multivariate time series to model two or more variables that influence each other using a differencing process. The Knowledge Discovery in Database (KDD) method was conducted for empirical analysis. Pre-processing is an analysis of raw climate data. The data mining process determines the proportion of each component of PCA and is selected as variables in the VARI process. The postprocessing is by visualizing and interpreting the PCA-VARI model. Variables of solar radiation and precipitation are strongly correlated with each measurement location data. A forecast of the interaction of variables between locations is shown in the results of Impulse Response Function (IRF) visualization, where the climate of the West Java region, especially the Lembang and Bogor areas, has strong response climate locations, which influence each other.
Recognition and Detection Methods of Artificial Intelligence in Computer Network Faults under the Background of Big Data

With the widespread use of computers and the rapid development of Internet technology, computer application technology has become more and more important in people’s work and life. The article mainly studies particle swarm optimization (PSO) and radial basis neural network function (RBF). Particle swarm optimization is an evolutionary swarm intelligence algorithm, such as nonderivative node transfer function or gradient information loss. Because its principle is simple and easy to implement, it can deal with some problems that cannot be solved by traditional methods. It is widely used in neural networks, and has achieved good results in many fields such as network training, performance optimization, and system mismanagement. RBF neural network is a feedforward neural network, which overcomes the shortcomings of traditional neural network learning process that the convergence is highly dependent on the initial value and can only be partially converged. This paper organically combines PSO algorithm and RBF neural network to study the detection and detection of computer network faults. The results show that although the prediction error of the improved network model on the experimental test set is still only 89.3% different from that of the SVM model, its convergence time is reduced to 0.90699885 s, which can effectively detect and identify computer network faults.
Quality Assessment of Physical Education Teaching in Colleges and Universities Based on Joint Neural Network

In order to improve the accuracy of physical education teaching quality evaluation in colleges and universities, a new JNE teaching quality evaluation model of physical education is proposed in colleges and universities. Firstly, a set of multi-index evaluation system of college physical education teaching quality based on analytic hierarchy process is constructed from the four dimensions of teaching content, teaching method, teaching attitude, and teaching effect. The score data are used as the input vector of joint neural network (JEN), and the quality level of college physical education (excellent, good, average, and poor) is used as the output vector of JNE, and a teaching quality evaluation model of college physical education courses based on the JNE model is established. Compared with PSO-JEN and JEN, it is observed that JNE has higher classification accuracy, specificity, and sensitivity for teaching quality evaluation of physical education courses in colleges and universities.
Intelligent Physical Training Data Processing Based on Wearable Devices

Intelligent processing of physical training data based on wearable devices is conducive to improving the efficiency and rationality of physical training. The current data processing methods cannot effectively extract the features contained in the data, resulting in low accuracy in tasks such as classification. This paper proposes an intelligent processing method for sports training data based on statistical methods and deep learning methods. First, the original data are preprocessed by some statistical methods to obtain the original feature vector. Then, the autoencoder model is used to extract the high-level hidden features in the original data. Finally, we input the extracted feature vector into a designed convolutional neural network classification model and generate the final classification result. Evaluation on the open Human Activity Recognition Using Smartphones Dataset shows that our proposed method achieves the best results compared with current methods.
Design and Implementation of a Machine Learning-Based English Intelligent Test System

Examination is a particularly important part of the students’ whole learning process. Therefore, this paper designs and develops an online examination system. This paper studies an examination recommendation algorithm based on a machine learning neural network model. The algorithm can recommend problems for students according to students’ error history, error prone, and knowledge poor problems. Experiments also show that the recommendation algorithm studied in this paper improves the recommendation effect compared with several other similar algorithms.
Informatization Teaching Mode of Vision Sensor Digital Image and Fusion Association Rule Mining Algorithm

Informatization teaching mode emerged under the tide of informatization development. The way of information dissemination and the process of human cognition have undergone tremendous changes in the era of informatization. Based on the vision sensor digital image and the fusion association rule mining algorithm, this paper studies the informatization teaching mode, aiming to analyze the influence of the vision sensor digital image and the fusion association rule mining algorithm on the foreign language teaching and enhance development decisions. The structure of digital image is analyzed by using image acquisition and processing technology and data transmission, and the digital image correlation method is explored. The use of data mining algorithms to conduct statistics on data integration related teaching documents in the past five years provides a strong basis for the data of the research process. In addition, in order to conduct a more detailed and comprehensive research on the informatization teaching model, the research scope was narrowed and the survey method was adopted to understand and collect data from relevant personnel and finally put forward constructive opinions for the purpose of the existing problems. The findings of the study demonstrated that the application of digital images can greatly expand the teaching content and greatly improve the creativity, experience, inclusiveness, and interactivity of teaching applications. The most obvious one is the sense of experience. The measured index has increased by 5.4 points.
Social Relationship Analysis Using State-of-the-art Embeddings

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents.
Self-sovereign Identity Management Framework using Smart Contracts

In centralized infrastructures, users are not capable of authenticating themselves, in identity management systems, beyond their application’s domain. Users are forced to trust their service providers for identification and data management. Such solutions have experienced large-scale data breaches and are cumbersome for users to remember the credentials for multiple sites. Furthermore, users have very little control over their data. The concept of decentralized identity has raised the possibility of better managing these concerns. It allows users to share only the relevant part of their personal information with a service provider to verify their digital identity. We propose OrgID, a decentralized identity and user-centric data management platform including identity registration and authorization procedures. Our approach supports self-sovereign identity architecture leveraged by blockchain. This method consists of a one-time proof-verification mechanism that facilitates the secure access and credibility of digital information. Moreover, users can maintain their identities associated with specific attributes and rely on a proof mechanism using smart contracts. It deploys a platform where user registration and authorization no longer involve a central service provider. We implemented the proposed solution using smart contracts on a private Ethereum blockchain. We further analyzed the performance of these processes regarding the system’s scalability to evaluate how they manage latency and the number of users. The results state that the system is highly scalable to manage a large number of users and the system’s latency is adjustable based on the application needs.
Toward a QUBO-Based Density Matrix Electronic Structure Method

Density matrix electronic structure theory is used in many quantum chemistry methods to "alleviate" the computational cost that arises from directly using wave functions. Although density matrix based methods are computationally more efficient than wave function based methods, significant computational effort is involved. Because the Schrödinger equation needs to be solved as an eigenvalue problem, the time-to-solution scales cubically with the system size in mean-field type approaches such as Hartree-Fock and density functional theory and is solved as many times in order to reach charge or field self-consistency. We hereby propose and study a method to compute the density matrix by using a quadratic unconstrained binary optimization (QUBO) solver. This method could be useful to solve the problem with quantum computers and, more specifically, quantum annealers. Our proposed approach is based on a direct construction of the density matrix using a QUBO eigensolver. We explore the main parameters of the algorithm focusing on precision and efficiency. We show that, while direct construction of the density matrix using a QUBO formulation is possible, the efficiency and precision have room for improvement. Moreover, calculations performed with quantum annealing on D-Wave's new Advantage quantum computer are compared with results obtained with classical simulated annealing, further highlighting some problems of the proposed method. We also suggest alternative methods that could lead to a more efficient QUBO-based density matrix construction.
Parallel Bookkeeping Path of Accounting in Government Accounting System Based on Deep Neural Network

“Parallel bookkeeping” is a key technical arrangement to achieve the goal of moderately separating and connecting the financial accounting system and budget accounting system established by the government accounting system. It is still a new thing for the majority of financial personnel in the government accounting subject. A deep neural network is the basis of deep learning. Up to now, the neural network has been applied in many fields, and its application in the financial field is more in-depth. The neural network is of great help to financial accounting. Integrating it into parallel bookkeeping in accounting can improve the work efficiency and accuracy of financial personnel. Through experimental analysis, it is found that its efficiency and accuracy are improved by 45% and 21.34% compared with the previous parallel bookkeeping path. The accounting parallel bookkeeping path based on the deep neural network studied in this paper not only has great practical significance for the work of financial personnel but also has far-reaching significance for the research of accounting paths in the future.
Artificial intelligence model driven by transfer learning for image-based medical diagnosis

Artificial intelligent (AI) systems for clinical-decision support are an important tool in clinical routine. It has become a crucial diagnostic tool with adequate reliability and interpretability in disease diagnosis and monitoring. Undoubtedly, these models are faced with insufficient data challenges for training, which often directly determines the model’s performance. In order word, insufficient data for model training leads to inefficiency in the model built. To overcome this problem, we propose an AI-driven model by transfer learning in accurate diagnosis for medical decision support. Our approach leverages the shortage of data with a pretrained model by training the neural network with a fraction of the new dataset. For this purpose, we utilized the VGG19 network as the backbone network to support our model in integrating known features with the newly learned features for accurate diagnosis and decision making. Integrating this trained model speeds up the training phase and improve the performance of the proposed model. Experimental results show that the proposed model is effective and efficient in diagnosing different medical diseases. As such, we anticipated that this diagnosis tool will ultimately aid in facilitating early treatment of these treatable diseases, which will improve clinical out-comes.
The Innovative Development of Russian Modern Oil Painting under the Background of Internet of Things and Artificial Intelligence

Contemporary Russian oil paintings have realized the inheritance and development of classical paintings. Although it is deeply influenced by European and American oil painting creations, it is commendable that it did not fully follow the creative framework of Western works, but realized the image of absorbing the essence of it and then integrating it into Eastern literature and art, and finally formed the integration of Chinese and Western oil paintings in Russian oil painting with a unique creative style. With the continuous development of science and technology, mankind has put forward higher-level requirements for nature, society, science, and technology, and many new things are rising in these aspects. As an emerging industry, Internet of things technology is becoming an important part of modern information technology. It can not only change the way and content of information dissemination and processing but also improve people’s quality of life and meet the growing material and cultural needs of the people. This paper first introduces the basic structure, style characteristics, and development process of Russian modern oil painting, then expounds the application of Internet of things technology and artificial intelligence technology, and then uses the method of questionnaire to investigate the innovation and development of Russian modern oil painting. Finally, the survey shows that senior students know more about Internet of things technology, while applied majors know more than non-applied majors. The grade that knows the least is freshmen, which is related to the degree of exposure to professional knowledge. Students majoring in Russian modern oil painting can share their painting experience through the Internet of things, learn online painting resources through the Internet of things, and add extra income to their part-time painters through the Internet of things. However, due to the Limited breeding technology of students, a small number of students can be part-time online painters, and most students use the Internet of things for knowledge input. Senior students majoring in Russian modern oil painting know more about artificial intelligence, and junior and sophomore students have a general understanding of this field. Through research, the team proposed the innovative development of modern Russian oil painting under the background of Internet of Things and artificial intelligence for reference, and formed our own oil painting creation style by integrating a variety of techniques.
Unsupervised Nuclei Segmentation Using Spatial Organization Priors

In digital pathology, various biomarkers (e.g., KI67, HER2, CD3/CD8) are routinely analyzed by pathologists through immuno-histo-chemistry-stained slides. Identifying these biomarkers on patient biopsies allows for a more informed design of their treatment regimen. The diversity and specificity of these types of images make the availability of annotated databases sparse. Consequently, robust and efficient learning-based diagnostic systems are difficult to develop and apply in a clinical setting. Our study builds on the observation that the overall organization and structure of the observed tissues are similar across different staining protocols. In this paper, we propose to leverage both the wide availability of haematoxylin-eosin stained databases and the invariance of tissue organization and structure in order to perform unsupervised nuclei segmentation on immunohistochemistry images. We implement and evaluate a generative adversarial method that relies on high-level nuclei distribution priors through comparison with largely available haematoxylin-eosin stained cell nuclei masks. Our approach shows promising results compared to classic unsupervised and supervised methods, as we quantitatively demonstrate on two publicly available datasets. Our code is publicly available to encourage further contributions ( https://github.com/loic-lb/Unsupervised-Nuclei-Segmentation-using-Spatial-Organization-Priors ).
MULTIRESOLUTION PATCH-BASED DENSE RECONSTRUCTION INTEGRATING MULTIVIEW IMAGES AND LASER POINT CLOUD

Abstract. A dense point cloud with rich and realistic texture is generated from multiview images using dense reconstruction algorithms such as Multi View Stereo (MVS). However, its spatial precision depends on the performance of the matching and dense reconstruction algorithms used. Moreover, outliers are usually unavoidable as mismatching of image features. The lidar point cloud lacks texture but performs better spatial precision because it avoids computational errors. This paper proposes a multiresolution patch-based 3D dense reconstruction method based on integrating multiview images and the laser point cloud. A sparse point cloud is firstly generated with multiview images by Structure from Motion (SfM), and then registered with the laser point cloud to establish the mapping relationship between the laser point cloud and multiview images. The laser point cloud is reprojected to multiview images. The corresponding optimal level of the image pyramid is predicted by the distance distribution of projected pixels, which is used as the starting level for patch optimization during dense reconstruction. The laser point cloud is used as stable seed points for patch growth and expansion, and stored by the dynamic octree structure. Subsequently, the corresponding patches are optimized and expanded with the pyramid image to achieve multiscale and multiresolution dense reconstruction. In addition, the octree’s spatial index structure facilitates parallel computing with highly efficiency. The experimental results show that the proposed method is superior to the traditional MVS technology in terms of model accuracy and completeness, and have broad application prospects in high-precision 3D modeling of large scenes.