Reviewerly/exHarmony huggingface dataset:
The use of modular-rating technology of teaching mathematics to students of Altai State Agricultural University

Introduction. New requirements are put forward for the organization of educational process, and improving the quality of training of graduates. Purpose setting. Therefore, teachers are faced with the need to apply modular-rating training technology which instantly reacts to changes in the situation in the labor market and can adjust the specialist model. It is democratic in its principles, content, and organization of the educational process. Modular-rating technology provides individualization of educational programs and the ways of their assimilation depending on the abilities and interests of students. Methodology of the study. The authors carried out modular structuring of the “Mathematics” course; methodological support (tests, individual tasks, etc.) of the modular-rating program was developed. A special role in the technology of modular-rating education is assigned to control over the knowledge of students, the authors used current, midterm and fi control. The quality of knowledge and skills of students in mastering the module was determined by various control methods: oral and written survey, control work, individual task, calculate and graphic work, etc. In addition, a testing method was used. To assess the effectiveness of student educational activities, a rating is used which is determined by points set in a selected unifi scale with a clear gradation of the level of theoretical knowledge, practical skills and abilities. The rating is formed during the entire period of the student training. Results. In modern conditions, higher education is faced with the tasks of forming a specialist with stable incentives for further professional self-improvement, activation and development of student cognitive abilities, differentiation of training content and individualization of pedagogical infl in accordance with the level of student training. Conclusion. The authors conclude that one of the effective ways to solve these problems is to train students according to the modular-rating system.
Inclusive model of additional education in creation and problems of educational trajectories for teachers

Introduction. The work contains a theoretical understanding of the problem of personal trajectories of education of teachers in additional inclusive education. Purpose setting. Work on the creation, implementation and improvement of the individual educational trajectory of schoolchildren and students with disabilities within the framework of an inclusive education model is the key condition for its success. Methodology of the study. This study is an attempt to implement a systematic approach to the study of problems in the practice of training modern teachers in the context of an inclusive education model. Results. The concept of an individual educational trajectory from the point of view of the task of high-quality training of a modern teacher in general and additional inclusive or inclusive education can be defined as an individualized (individual-specific) way for a person to search for and implement his personal, interpersonal and professional potential, his creative and reproductive, pedagogical and other abilities, implemented with the support and participation of a tutor, supervisor or other mentor, or completely independently (in the context of self-education). The created and implemented inclusive trajectory, being presented in the didactic and mentoring interaction of the teacher (subject teacher, class teacher/curator, or other tutor) and the student, requires work on its reconciliation, reflection, correction, etc. The activation of educational and professional activity of students with disabilities is associated with the formation and implementation of an inclusive individual educational trajectory as such a trajectory of personal, interpersonal and professional formation and development of a person, which is designed to solve the problems of his inclusion. It can take different forms. Conclusion. An educational trajectory that activates and creates conditions for the comprehensive development and inclusion of a person with disabilities in the community is formed and implemented under the condition of synchronization of the lines of personal, interpersonal and educational and professional improvement in general and additional inclusive education, harmonization of the space-time of education and the space-time of being of each person with disabilities as a student and future specialist. Such synchronicity and harmony is the result of conscious, multilateral and constant efforts of all subjects of education at all stages of development and implementation of the educational trajectory of the future teacher, up to reflection and correction of the educational route and its different stages.
Novel Shuffling Countermeasure for Advanced Encryption Standard (AES) against Profiled Attack in Mobile Multimedia Services

Mobile multimedia services are gaining popularity among many users by developing wireless communication and mobile devices. Mobile multimedia has alleviated conventional multimedia’s time and space limits, making it easier for consumers to access services and meet content demands. However, cyber risks lie in the shadows of the expansion of mobile multimedia services, threatening to continue wreaking havoc. Although various methods exist to defend against these cyber threats, side-channel analysis has remained a critical challenge in the current approaches that rely on cryptographic algorithms. Nowadays, research on deep learning-based side-channel analysis is receiving much attention. Attacks are constantly performed against implementations, to which existing countermeasures against traditional side-channel analysis are applied, using various artificial neural network structures. However, while studies on the implementations to which masking and simple hiding schemes using jitter are active, studies on the implementations to which the shuffling scheme or the random insertion of dummy operations scheme are applied have been relatively less attention. In a previous study, Lee and Han has used deep learning to distinguish between real and dummy operations in an implementation that combined shuffling scheme and random insertion of dummy operations scheme. They also proposed countermeasures against their attacks. However, they did not choose an appropriate environment that is as close to noise-free as possible, and their countermeasure still has flaws. Therefore, in this study, we analyze the causes of vulnerability of the previous countermeasure and propose a novel countermeasure that can completely solve them. The novel countermeasure is a method of uniformly applying shuffling schemes and random insertion of dummy operation schemes to byte-independent and byte-dependent operations of an advanced encryption standard, respectively. It was confirmed that our countermeasure is safe from attackers who perform profiled attacks even in an experimental environment with almost no noise.
The Riverine Archive: Nausea and information loss on the neoliberal ship of fools

Within academia, as in other corporatized environments, there are irreconcilable tensions embedded in the managerial data imaginary, in contrast to the messy reality of lived experience and increasingly precarious working conditions for those at the coalface of Higher Education. Combined with student debt and escalating surveillance via so-called ‘artificially intelligent’ transactional data analysis, fantasies of control and domination converge on platitudes about Big Data and computational information, often presented as unambiguously neutral via idealized visualizations and ‘dashboards’ such as those commonly provided by Tableau and Google. As a structure, the digital archive is no different, open to fantasies of secure representation but, in fact, always unstable, subject to the materiality and flux of electronic, symbolic and social processes. As academics exposed to intensified metricization within increasingly data driven institutions, how can we counter the reduction of our qualitative research and experience to dashboards and scores? Drawing upon the work of Fleming’s Dark Academia: How Universities Die (2021), Poster’s The Mode of Information (1990), Cascella’s En Abime: Listening, Reading, Writing: An Archival Fiction (2012) and Bayne et al.’s The Manifesto for Teaching Online (2020) among others, and focusing on work developed by the authors from 2008 onwards, they discuss their contingent, often nauseating virtual reality information repository, the Riverine Archive, developed to hold and withhold information about their writing and art within the ugly hulk of what they define as a ‘neoliberal ship of fools’. The work seeks to offer a counter to prevailing platitudes and fantasies about the neutrality and realism of data to directly represent a stable, singular reality. The authors concur that information is not beautiful, but rather a fallacy of stability, servicing a rapacious, anti-academic neoliberal ideology that needs to be brought to the surface and subjected to honest discourse, away from hype and managerial wishful thinking. Truth and beauty cannot be framed as stable, monolithic or universal; to do so is to replicate a colonial projection of knowledge, a mono-logic, centring all truth upon the Global North.
On the issue of prevention of digital addiction of students of secondary professional educational organizations

Introduction. The article examines the problems associated with the digitalization of the education system of secondary vocational education and the impact of this process on students of secondary specialized educational organizations. The paper points out that along with the obvious benefits associated with the need to train personnel for the digital economy by means of using Information and Communications technologies, the digitalization of the education system of the vocational education system has certain risks and costs. Purpose setting. The solution to the problem of digital addiction is based on the development and implementation of a set of measures aimed at its prevention. Methodology of the study. Theoretical methods, comparative analysis and generalization of scientific literature on the problem of studying digital addiction and prevention methods were used; diagnostic methods, methods of result processing. Results. The negative impact of total digitalization on students of secondary vocational education is considered, the emphasis is placed on the fact that along with the advantages, digital educational technologies have a negative impact on personality formation under the condition of uncontrolled use. First of all, this is due to the unpreparedness of the education system to neutralize the level the negative impact of this phenomenon on the personality of students in colleges, schools and other secondary specialized educational institutions. The article points to the need to form a new digital culture in the digital educational environment, the so-called «cyberculture» based on the prevention of digital addiction and compliance with the rules of digital hygiene. The analysis of the impact of the digital educational environment of educational institutions on students determined the need to study the phenomenon of digital hygiene as a factor in preventing the digital dependence of the subjects of the educational process. The literature devoted to the negative impact of the digitalization of the educational process on the mental, physical and intellectual development of students is analyzed. Conclusion. The article reveals the problem of safe behavior in the digital educational space, the issues of leveling the negative processes caused by the digitalization of education through the implementation of a set of measures to prevent digital addiction. The authors have developed and proposed a set of pedagogical conditions for the prevention of digital addiction of students of secondary vocational education.
Application of Dynamic Process Neural Network Model Identification in Ethnic Dance Online Teaching System

With the development of the times, education presents a new trend, but the teaching characteristics of dance classroom teaching cannot adapt to the current development trend. In this article, the author analyzes modern information technology, hoping to realize the teaching of folk dance on the Internet and provide a new model of online distance teaching for folk dance teaching. The author analyzes the current teaching problems in colleges and universities, and proposes to change the existing teaching situation based on dynamic process neural network model identification and artificial intelligence, and instead use online remote network ethnic dance teaching. Online distance education can enable flexible teaching of folk-dance courses, deeply dig into the theoretical basis of distance teaching, and use online distance network teaching to make teaching time more flexible, not only providing new teaching methods but also introducing new teaching concepts. Based on the traditional neural network model identification, a dynamic process neural network model identification is developed. This model is no longer subject to the input limitation of the traditional neural network model, the processing time is relaxed, and the advantages are more obvious. In this research, the author introduces dynamic process neural network model identification in time series data mining, and makes full use of artificial intelligence to deeply analyze the classification and prediction problems in the context of time series.
Autonomous deck landing of a vertical take-off and landing unmanned aerial vehicle based on the tau theory

The research on autonomous landing of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) is well established. However, the research on autonomous deck landing using visual methods is relatively not so mature and many of them require the support of ground infrastructures. In order to reduce such dependencies, a ship landing guidance strategy based on on-board vision is studied. Considering the characteristics of the ship landing issue, we propose a three-phase landing scheme and a decision-making method to ensure landing safety is also studied. For improving the traditional two-dimensional (2D) optical-flow method, a three-dimensional (3D) velocity vector estimation method using image spherical optical flow is studied. Furthermore, a guidance law based on the tau theory is employed by only using the visual information of the line of sight to the target. In this phase, a trajectory-tracking controller is applied to generate the velocity commands of the UAV. Finally, the whole algorithm is validated by simulation in different wave conditions developed with Unity3D. Compared with traditional trajectory planning methods, our method does not require complex optimization iterations and can meet both the real-time and accuracy requirements of deck landing. The average tracking error of our method maintains in 0.2 m. Moreover, the whole algorithm runs efficiently at around 30 fps on a Raspberry-Pi 3B+ microcomputer which meets the real-time requirements.
An improved nonlocal means‐based correction strategy for mixed noise removal

Noise removal is a classic problem. Most researchers focus on Gaussian noise removal due to the regularity of the noise distribution, while mixed noise removal is always challenging because of the uncertainty of the noise distribution. Mixtures of additive white Gaussian noise (AWGN) with salt-and-pepper impulse noise (SPIN) and mixtures of AWGN with random-valued impulse noise (RVIN) are typical examples of mixed noise. Most mixed noise removal methods are effective in the removal of mixed AWGN and SPIN, but perform poorly in the removal of AWGN and RVIN. The main reason is the randomness of RVIN, which leads to poor denoising performance when the RVIN is strong. In this paper, an improved nonlocal means-based correction strategy (INS) is proposed. In INS, an improved nonlocal means strategy is applied to replace the impulse noise pixels to make the mixed noise obey an approximate Gaussian distribution. To prove the validity of INS, a convolutional neural network (CNN) in combination with INS (CNNINS) is applied to remove mixed noise. Experimental results are used to compare the proposed CNNINS with the most advanced mixed noise removal methods.
The dummy‐based trajectory privacy protection method to resist correlation attacks in Internet of Vehicles

Summary Existing dummy‐based trajectory privacy protection schemes do not take into account the correlation of multiple locations and whether the generated trajectory based on dummies matches the user's movement mode, which enables the adversary to identify some dummies. Aiming at this problem, to ensure that the generated trajectories match the movement modes of users, historical query trajectories of users are selected. In this way, the generated dummies on the selected historical query trajectories are based on the location relationship of adjacent time and the background information constraint of the dummy, namely, they should meet the time reachability, the similarity of historical query probability and the maximum in‐degree. Security analysis shows that the proposed scheme effectively perturbs the spatiotemporal correlation between the real location and dummies. Furthermore, the proposed scheme is compared with the existing schemes in terms of single‐point location exposure risk and trajectory exposure risk, and the experimental results indicate that the proposal has significant improvement in location privacy protection of the user.
Potential of hyperspectral-based geochemical predictions with neural networks for strategic and regional exploration improvement

This paper summarises an evaluation of the application of artificial intelligence to hyperspectral drill-core scans for more effective mineral exploration. The dataset used was based on publicly available core scans and related geochemical analysis from Australia. Prior to unification, a detailed quality assessment of the geochemical data was undertaken. Special focus was paid to gold, silver, copper, iron, uranium, nickel, lead, tin, antimony, arsenic and bismuth contents. The dataset was labelled with defined ore grades related to economic cutoff values. The impact on predictions of different setups is related to the amounts of data used for learning, data design and implementation of the geological domains. Based on 1-metre bins, the results from more than 700 km of drill cores were used and analysed with the potential for geological exploration in different scenarios discussed. The results indicate the enormous potential of the use of hyperspectral scans in combination with artificial intelligence for the development of exploration scenarios and to provide support for exploration geologists and target detection. The application of predictors on scanned drill cores from Australia also indicates mineralised zones that have not been analysed chemically for all metals above economic cutoffs. This result shows the enormous potential of the approach for strategic exploration but also mining operations. Prediction of geochemical concentrations for gold, copper and iron based on a neural network in drill cores is possible. Using mineral abundances from hyperspectral core scans as learning records, and existing elemental geochemical analyses as labels, the predictions are given with an accuracy of better than 80–90%.KEY POINTSThe trained artificial intelligence system has for the first time enabled direct estimation of metal grades from hyperspectral scans.It also shows potential for applications to analyse airborne hyperspectral data for direct mapping of metal grades.Finally, it may pave the way for better plant management by the usage of hyperspectral data for direct grade estimations in operational mining and ore sorting.
A Grammatical Error Correction Model for English Essay Words in Colleges Using Natural Language Processing

Natural language processing technology is a theory and approach for exploring and developing successful human-computer communication. With the rapid growth of computer science and technology, statistical learning methods have become an important research area in artificial intelligence and semantic search. If there are errors in the semantic units (words and sentences), it will affect future text analysis and semantic understanding, eventually affecting the whole application system performance. As a result, intelligent word and grammatical error detection and correction in English text are a significant and difficult aspect of natural language processing. Therefore, this paper examines the phenomena of word spelling and grammatical errors in undergraduate English essays and balances the mathematical-statistical models and technology solutions involved in intelligent error correction. The research findings of this study are represented in two aspects. (1) In nonword mistakes, four sorts of errors are studied: insertion, loss, replacement, and exchange between letters. It focuses on nonword mistakes and varied word forms (such as English abbreviations, hyphenated compound terms, and proper nouns) produced by word pronunciation difficulties. This paper utilizes the nonword check information to recommend an optimum combination prediction method based on the suggested candidate list for actual word errors, and the genuine word repair model is trained. This approach is 83.78% accurate when used with actual words with spelling errors in the context. (2) It verifies and corrects sentence grammar using context information from the text training set, as well as grammatical rules and statistical models. In addition, it has investigated singular and plural inconsistency, word confusion, subject, and predicate inconsistency, and modal (auxiliary) verb errors. It includes sentence boundary disambiguation, word part-of-speech tagging, named entity identification, and context information extraction. The software for checking and fixing sentence grammatical mistakes presented in this article works on English texts with difficulty levels 4 and 6. Furthermore, this work obtains a clause correctness rate of 99.70%, and the system’s average corrective accuracy rate for four-level and six-level essays is more than 80%.
An Intelligent Black Widow Optimization on Image Enhancement with Deep Learning Based Ovarian Tumor Diagnosis model

Ovarian tumour is a commonly affecting gynaecologic malignancy which necessitates effective image processing techniques for accurate diagnosis. This article presents an intelligent IE with a deep learning-based ovarian tumour diagnosis (IEDL-OVD) model. The goal of the IEDL-OVD model is to enhance the quality of the input medical image, thereby improving the diagnostic outcomes. The proposed IEDL-OVD model includes a black widow optimisation-based IE technique. In addition, the VGG16 model is applied as a feature extractor and a stacked autoencoder (SAE) is utilised as a classification model to determine the existence of the ovarian tumour. In order to inspect the diagnostic outcome of the IEDL-OVD model, an elaborative experimentation analysis is performed and the results are examined in terms of different evaluation parameters. With 100 images, the IEDL-OVD model has obtained an increased precision rate and recall rate of 0.735 and 0.612. The black widow optimization algorithm (BWOA) on the quality IE has achieved a maximum contrast of 0.97, a contrast-to-noise ratio (CNR) of 92.74%, a weighted peak signal-to-noise ratio (WPSNR) of 20.43 and a homogeneity of 0.94.