Reviewerly/exHarmony huggingface dataset:
Classification and Recognition of Internet Literature Text Based on Data Mining Technology

With the rapid spread and development of the Internet, Internet text has also become the main source of information and receiving source in human daily life, and how to obtain more valuable information and knowledge from this information has also become an urgent problem to be solved. This article has carried out research on the classification and recognition of Internet literature text based on data mining technology. It understands the relevant theories of Internet literature text classification and recognition on the basis of literature data, and then designs the classification and recognition of Internet literature text based on data mining technology. In order to increase the accuracy of the analysis, the weighting method in classification and recognition was improved, and then tested to prove it. The result showed that the improved weighting method is more accurate in the same dimension than before the improvement, and it is in the four types of weighting. Among the methods, the accuracy is the largest and the classification effectiveness is better.
CONTENT BASED IMAGE RETRIEVAL USING MULTI-SVM TECHNIQUE FROM NOISY AND NON NOISY IMAGES.

Image mining is a technique which handles the mining of information, image data association, or additional patterns not unambiguously stored in the images. It exploits methods from computer vision, image retrieval, image processing, data mining, machine learning, database, and artificial intelligence. In the proposed work, we have developed a new system that can retrieve the images from a dataset on the basis of contents of the query image. Here, ‘Content-Based’ means that the search will analyze the actual contents of the image. The existing system does not evaluate the results upon attacks but in proposed system the results are also being evaluated on adding noise to the images and blurring the images. The overall average accuracy of the proposed system is 96% whereas that of existing system is 85%. Performance of the existing systems is checked on the maximum of 1000 images whereas the performance of the proposed system is checked on more than 5000 images.
Dziga Vertov and Walt Whitman: Poetic Image in Documentary Films

Introduction . The article is devoted to the little-studied issue of the influence of the American poet Walt Whitman on the work of the Soviet film director Dziga Vertov. The creative world of films and literary experiences of the documentary filmmaker is largely inspired by the images of American poet. Methodology and sources . On the basis of Vertov`s scripts, poems and archival documents, author analyzes the phenomenon of the director`s dialogue with Whitman and his poetry. In a comparative analysis of the aesthetics of the two artists, revealed that the philosophical ideas and imaginative system of the American poet were objects of artistic reflection on Vertov`s documentary films. Results and discussion . The article analyzes the leitmotifs of the work of Dziga Vertov, which, according to the author, were born under the great influence of the poetry of Walt Whitman. The imagery and ideas of the American poet`s works emerged in the director`s texts in the early 1920s. In his first manifestos Vertov introduced the image of an “electric man”, which in many ways correlates with the analogous one in Whitman`s lyrics. The author also points out that the Old Testament Adam appears in the texts and verses of Vertov under the direct influence of the American. The article examines the model of state and society in the works of Vertov and Whitman. The author finds similarities between the two artists in relation to the individual, the desire for equality and emancipation, the search for a spiritual principle in collectivism. The images of Lenin and Lincoln are analyzed separately. The semantic parallels between them in the poetics of the Vertov and Whitman are revealed and shows their direct comparisons in the genre, imagery and approaches. Conclusion . Analysis and comparison of images and motives of the work of Dziga Vertov and Walt Whitman allows to reveal new facets of the artistic world of the Soviet film director. The author notes that the ideological basis of his paintings often hid genuine creative intensions to search for intertextuality, to reveal the poetic image in documentary film.
Machine Learning for Identifying Abusive Content in Text Data

The proliferation of social media has created new norms in society. Incidents of abuse, hate, harassment and misogyny are widely spread across social media platforms. With the advancements in machine learning techniques, advanced text mining methods have been developed to analyse text data. Social media data poses additional challenges to these methods due to their nature of short content and the presence of ambiguity, errors and noises in content. In the past decade, machine learning researchers have focused on finding solutions dealing with these challenges. Outcomes of these methods boost the social media monitoring capability and can assist policymakers and governments to focus on key issues. This chapter will review various types of machine learning techniques including the currently popular deep learning methods that can be used in the analysis of social media data for identifying abusive content.
Fabrication of superconducting qubits and auxiliary devices with niobium base layer

Over the past two decades significant advances have been made in the research of superconducting quantum computing and quantum simulation, in particular of the device design and fabrication that leads to ever-increasing superconducting qubit coherence times and scales. With Google’s announcement of the realization of “quantum supremacy”, superconducting quantum computing has attracted even more attention. Superconducting qubits are macroscopic objects with quantum properties such as quantized energy levels and quantum-state superposition and entanglement. Their quantum states can be precisely manipulated by tuning the magnetic flux, charge, and phase difference of the Josephson junctions with nonlinear inductance through electromagnetic pulse signals, thereby implementing the quantum information processing. They have advantages in many aspects and are expected to become the central part of universal quantum computing. Superconducting qubits and auxiliary devices prepared with niobium or other hard metals like tantalum as bottom layers of large-area components have unique properties and potentials for further development. In this paper the research work in this area is briefly reviewed, starting from the design and working principle of a variety of superconducting qubits, to the detailed procedures of substrate selection and pretreatment, film growth, pattern transfer, etching, and Josephson junction fabrication, and finally the practical superconducting qubit and their auxiliary device fabrications with niobium base layers are also presented. We aim to provide a clear overview for the fabrication process of these superconducting devices as well as an outlook for further device improvement and optimization in order to help establish a perspective for future progress.
CHALLENGES OF TEACHING ENGLISH ONLINE DURING THE COVID -19 PANDEMIC (ON THE EXAMPLE OF THE SAMTSKHE-JAVAKHETI REGION SCHOOLS)

The article deals with the problems created during the online learning of English during the Covid 19 pandemic in Georgia. The reasarch was conducted with English language teachers in Samtskhe-Javakheti region. The article discusses all the joint steps taken by the Government of Georgia and the Ministry of Education that have become necessary. Among them is a remarkable project such as "Teleskola". The report highlights the ongoing processes in the world during the pandemic. We also talk about the experience of many countries around the world when they had to "switch to emergency distance learning" with the closure of schools.The study identifies all the challenges and issues faced by English language teachers in Samtskhe-Javakheti. In addition to the fact that the region is multiethnic, it is noteworthy that teachers from both urban and rural minority schools participated in the study. The results of the challenges arising during the online English language teaching in Samtskhe-Javakheti schools are presented in details. All indicators are calculated as a percentage with statistical accuracy. The aim of the survey is to show what challenges English language teachers faced in the Samtskhe-Javakheti region (Georgia) during the pandemic (2020-2021 school year), the methods and strategies they used in their online teaching, and their experiences that helped them to overcome their difficulties.Teaching languages during a pandemic requires teachers' creativity as students in the 21st century need to be critical thinkers, creative, sociable, collaborative, tolerant. To achieve this, a teacher must combine four key skills to teach students language and master with the 21st century skills. The article also includes some basic tips that a teacher can follow when creating an "emergency distance learning".The current situation has shown us that neither the state nor the teacher should stop thinking, caring and planning the learning process. An online teaching format cannot completely change the benefits of face-to-face teaching, however, a better-prepared teacher's lesson is a prerequisite for a better day.
The COVID-19 pandemic and upgrades of CI speech processors for children: part I—procedure of speech processor upgrade

To demonstrate the feasibility of upgrading speech processors during the COVID-19 pandemic.Adopting concepts from "lean thinking", we optimized hospital workflows to allow speech processors to be upgraded despite the obstacles arising from national guidelines for COVID-19 prevention. The study involved 297 children, aged from 7.3 to 18.0 years, whose processors were upgraded on five consecutive Saturdays during a time of peak COVID-19 in Poland.The optimized workflow allowed us to conduct speech processor upgrades during a time of peak COVID-19 in Poland. The upgrades were conducted as scheduled, patient flow was smooth, appropriate social distancing was kept, and no reports of COVID-19 infection in our patients in the 2 weeks after their visit were received.Upgrading of speech processors in children is still feasible under coronavirus conditions.
An Evaluation of the Automatic Detection of Hate Speech in Social Media Networks

Numerous approaches have been developed over recent years to detect hate speech on social media networks.Nevertheless, a great deal of what is generally recognized as hate speech cannot yet be detected.There remain many challenges to assuring the effectiveness and reliability of automatic detection systems in different languages, including Arabic.Social media platforms and networks such as Facebook continue to encounter difficulties regarding the automatic detection of hate speech in Arabic content.Given the importance of developing reliable artificial intelligence and automatic detection systems that can reduce the problems and crimes associated with the spread of hate speech on social media platforms, this study is concerned with evaluating the performance of the automatic detection and tracking of hate speech in Arabic content on Facebook.As an example, the study evaluates the period in October 2020 that came to be known as France's cartoon controversy.Two different corpora were designed.The first corpus comprised 347 posts deleted by Facebook, now known as Meta.The second corpus was composed of 1,856 posts that were randomly selected using the hashtag ‫الله‬ ‫رسول‬ ‫إال‬ (except the Prophet of Allah).The results indicate that there is a considerable amount of hate speech taken from or influenced by the Islamic religious discourse, but that automatic detection systems are unable to address the peculiar linguistic features of Arabic.There is also a lack of clarity in defining what constitutes "hate speech".The study suggests that social media networks, including Facebook, need to adopt more reliable automatic detection systems that consider the linguistic properties of Arabic.Political thinkers and religious scholars should be involved in defining what constitutes hate speech in Arabic.
Varibox encryption algorithm : The new generation of hybrid security measure for the era of quantum computation

The continual proliferation and evolution of the Internet of Things (IoT) domain is posing serious security threats to the whole network. In IoT, the insecure communication channel between sensors and the Internet can lead to malicious penetrations. Poor computational resources in traditional sensors critically constraint and impact the execution of conventional security algorithms among IoT devices. Consequently, lightweight security algorithms turn out to be a feasible alternative. To keep up with the existent Internet security level, this work proposes a new security scheme that employs a unique methodology based on a one-way counter value hashing table i.e. VariBox. This scheme integrates modular arithmetic applied on Unicode UTF-16 encoding standards coupled with AES and RSA algorithms. The proposed methodology effectively offers i) low computational overhead i.e. overall processing time of 0.1 seconds (VariBox processing time 0.003 seconds) and ii) enhances the overall security of the IoT data transfer model. Once this model is employed, the overall spectrum of potential brute force solutions will be rendered computationally expensive and infeasible to be calculated within a finite time period.
Analysis of Student Performance Based on Differential Privacy Protection

In the context of the information age, the use of data has greatly promoted the development of society.Education is inseparable from the development of the country， and education has become an area of close concern to society.How to maximize the useful value of mining data while protecting the privacy contained in the data is a problem we need to pay attention to.Based on the test scores of students in multiple courses, this paper analyzes the Apriori algorithm in the correlation analysis and mining, and obtains the correlation between the courses and the interval distribution of test scores.On this basis, we filter the obtained association rules, and use the Laplacian mechanism to increase noise interference to the filtered association rules, and protect the privacy of students through differential privacy.While using the maximum value of data as much as possible to promote the development of targeted teaching work, the privacy of students' performance is protected.
Fuzzy Cognitive Maps Applied in Determining the Contagion Risk Level of SARS-COV-2 Based on Validated Knowledge in the Scientific Community

Due to the current pandemic that is causing psychological problems, sequelae, in some cases, irreparable damage, and, mainly, leading people around the planet to death; this work aims to create an intelligent application from a validated table, presented by Texas Medical Association. Specifically, the application of fuzzy cognitive map can facilitate the contagion risk level’s inference of SARS-CoV-2 from information on human behavior of everyday life. As a possible contribution of this investigation, in addition to the listed and classified risks, the individual’s behavior should mitigate or increase his contagion risk level. The results are presented and normalized on a scale from 0 to 10.
Intelligent Analysis and Processing System of Financial Big Data Based on Neural Network Algorithm

With the continuous development of computer technology, data mining has gradually become an indispensable part of enterprise information management. Traditional enterprises have been unable to meet the needs of modern economy and Society for talents, and big data, as a new technology, appears in the field of internal information processing. Financial analysis is to comprehensively evaluate the company’s daily business activities, comprehensively grasp and reasonably allocate resources, so as to improve efficiency and reduce costs. Therefore, using neural network algorithm to realize big data prediction is more and more important and practical for modern enterprises. This paper first expounds the concept of financial big data and the characteristics of data intelligent analysis, and then introduces the application of neural network model. Then, it will study based on financial big data, design a complete and effective neural network intelligent algorithm system that can be applied in practice, and test the effectiveness of financial big data intelligent analysis and processing system. Finally, the experiment shows that the difference between the predicted value and the actual error value of the system data is small, and the effectiveness is very high. At the same time, the performance of data processing meets the needs of users, which can improve work efficiency and reduce labor cost.
PRM: Pose Recalibration Module for Action Recognition

Two-stream convolutional network is the mainstream method of human action recognition, which can achieve excellent recognition precision in most datasets. However, the challenge of two-stream convolutional network is that it can not model human action well. To tackle this issue, this paper proposes Pose Recalibration Module (PRM) to better model human action features. The proposed PRM is composed of three components: (1) Part Affinity Fields pose estimator Module to explicitly capture human actions and generate human joint heatmap, (2) Multi-scale Action Extraction Module to construct different scales of joint heatmap sequence at different time intervals, and (3) Action Classification Module to classify action. Finally, this paper arranges late fusion strategy to fuse pose modalities and the two-stream network to obtain the final classification score. Experimental results on UCF101 and HMDB51 show that our approach can boost the performance of two-stream network. Meanwhile, our method obtains a competitive performance compared with state-of-the-art methods.
Content moderation as language policy

Commercial content moderation removes harassment, abuse, hate, or any material deemed harmful or offensive from user-generated content platforms. A platform’s content policy and related government regulations are forms of explicit language policy. This kind of policy dictates the classifications of harmful language and aims to change users’ language practices by force. However, the de facto language policy is the actual practice of language moderation by algorithms and humans. Algorithms and human moderators enforce which words (and thereby, content) can be shared, revealing the normative values of hateful, offensive, or free speech and shaping how users adapt and create new language practices. This paper will introduce the process and challenges of commercial content moderation, as well as Canada’s proposed Bill C-36 with its complementary regulatory framework, and briefly discuss the implications for language practices.
A Survey of Object Tracking Methods Based on Deep Learning

The object tracking problem is usually represented as estimating the state of an arbitrary target in a video only given its state in the initial frame. Many recent object tracking methods based on deep learning have achieved advanced performance. In general, most object tracking methods can be divided into two types. One family of trackers such as DiMP, ATOM learn a discriminative classifier, which adopt online updating strategy, to distinguish the target object from the background. In the process of tracking, these methods need to update the model parameters using reference frames and states. The other family of trackers represented by Siamese network use the reference state and the current frame as the input of the model without changing or updating the parameters. These trackers calculate the cross-corrlation between the reference frame and the current frame to complete the object tracking task. Both methods usually divide object tracking task into two subtasks: classification and estimation. The target location is maintained by target classification, and the accurate target boundary box is obtained by target estimation. In this paper, we will investigate some state-of-the-art object tracking methods using deep learning in recent years. These methods will be introduced in three different emphases: classification, estimation, both.
Research on the Anti-risk Ability Intelligent Evaluation System of Distributed Data Centers without Human Intervention

Using the association rules of the basic data of the whole link, the operation history law scenarios and the operation status risk perception methods among the distributed data centers are respectively studied. Modeling methods can be used to summarize the normal operation rules of various systems, and the operation rules can be used to find abnormal operation, and then determine the risk content. Its operation can be divided into different regularity stages to be distinguished, grasp the proportional relationship of each resource load in each stage, and realize the quantification of the regularity as a data index. Perform simulation tests to verify the effectiveness of the proposed method. Realize the quantification of abnormalities as data indicators, and then realize the conversion to risk judgments.
Minimum cost flow phase unwrapping method considering range direction slope

In mountainous terrain, the synthetic aperture radar (SAR) interferometry technology causes serious spatial decorrelation due to the shadows and overlaps in the range direction. We propose a minimum cost flow phase unwrapping method considering range direction slope (RDS). The RDS is calculated in the SAR imaging direction, and the RDS is used to divide the high- and low-quality regions of the interferometric phase. In the high-quality area, the residual error between the estimated value of the wrapped phase and the true value is greatly affected by the RDS. The quality map based on the slope in range direction is introduced to limit the number of residuals, at the same time, the quality map is used as the unit cost of the flow to balance the residuals of each node in the minimum cost flow, achieve the minimum cost of the minimum cost flow network, so as to achieve the minimum residual estimation and complete the unwrapping. The phase unwrapping experiments are carried out with the simulated data and the real data from Sentinal-1A in Qinling Mountain in China, and the experimental results show that the root-mean-square error of this method is lower than that of the path tracking method, the least squares method, and the traditional minimum cost flow method. The proposed method is suitable for phase unwrapping in complex mountainous areas.
The Following System of 3D Simulation Manipulator

In this paper, the three-dimensional motion camera of Kinect sensor is used to scan the space where the human body is to capture human movements in real time, and in the upper machine processing platform real-time display of the human body movement state, analyze the action instructions corresponding to different actions of the human body, and complete the 3D simulation manipulator following the actions of the human body. After debugging, the system designed in this paper can accurately recognize gestures without obvious delay, and the manipulator can follow the human arm well.