Reviewerly/exHarmony huggingface dataset:
Research on Blockchain Architecture and Operating Principles Based on H-DAG

A hybrid blockchain structure (hybrid directed acyclic graph, or H-DAG) is proposed in this article to solve the existing problem of blockchain architectures using symmetric key encryption technology by combining the characteristics of single-chain blockchains and DAG distributed ledgers. By improving the block and transaction structures and optimizing the consensus mechanism, the H-DAG confirmed transaction orders while maintaining the high-throughput characteristics of a DAG, thus solving the transaction order dependence problem. We introduced a lightweight PoW mechanism to the H-DAG to improve the anti-fork ability of the blockchain. An incentive mechanism was adopted in our model to compel honest nodes to be more enthusiastic about participating in, maintaining, and enhancing the security of a given network. The blockchain states achieved strong levels of consistency, and their transaction confirmation times were predictable. We evaluated the performance of the H-DAG by comparing and analyzing multiple experiments, and we modeled a forking attack strategy, verifying the resistance of the H-DAG to this attack strategy. The experimental results demonstrated that the order of transactions in the H-DAG was globally consistent, and the confirmation time of transactions was predictable. The H-DAG improved the anti-fork ability and enhanced the security of the blockchain to ensure a degree of decentralization of the blockchain system. Therefore, the system throughput was enhanced by improving the block structure using symmetric key technology.
Demonstration Exam in Continuing Entrepreneurial Education within the VET and HE Systems

Achieving the national development goals of the Russian Federation and maintaining the sustainability of the economy in the face of sanctions pressure impose special requirements on the development of entrepreneurial competencies among students of postsecondary education. One of the key innovations of the last decade in the field of entrepreneurial education in Russia was the introduction of a demonstration exam (DE) based on WorldSkills Russia methodology. Its results reveal high heterogeneity in the quality of entrepreneurial competencies acquisition among the students. Meanwhile, the experience of educational organizations that effectively cope with mass preparation for DE has not yet been sufficiently studied. Moreover, the specifics of the formation of entrepreneurial competencies in VET have not been studied, despite the steady growth in the share of college graduates in the university applicant base. In order to ensure the continuity of entrepreneurial education for students studying along the VET–HE trajectory, as well as to study the best practices for the formation of entrepreneurial competencies in the prism of DE, qualitative research of the search type was implemented. The study was carried out based on qualitative methodology. Within its framework, in-depth semi-structured interviews were conducted with expert teachers from four educational institutions with the highest results in the DE. For the triangulation? The data also included 4 in-depth semi-structured interviews with VET students who have successful experience in confirming the results of mastered entrepreneurial competencies. The interview analysis was based on a mixed approach. Structural and procedural components are identified and described, showing ways to integrate entrepreneurial education into traditional VET and to create a comprehensive educational environment. Interactive components used in teaching and learning entrepreneurship are investigated. According to the research results, as in the case of leading foreign practices, the best domestic practices are characterized by the use of constructivist pedagogy, while the key teaching method is long-term interdisciplinary student projects. The specificity of the formation of entrepreneurial competencies in VET has been revealed – this is a practice orientation and an orientation towards routine entrepreneurship. The results obtained can be used by practitioners to improve the quality of entrepreneurial education in VET schools.
Particle Shape Recognition with Interferometric Particle Imaging Using a Convolutional Neural Network in Polar Coordinates

A convolutional neural network (CNN) was used to identify the morphology of rough particles from their interferometric images. The tested particles had the shapes of sticks, crosses, and dendrites as well as Y-like, L-like, and T-like shapes. A conversion of the interferometric images to polar coordinates enabled particle shape recognition despite the random orientations and random sizes of the particles. For the non-centrosymmetric particles (Y, L, and T), the CNN was not disturbed by the twin image problem, which would affect some classical reconstructions based on phase retrieval algorithms. A 100% recognition rate was obtained.
A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning

The continuous path of a manipulator is often discretized into a series of independent action poses during path tracking, and the inverse kinematic solution of the manipulator’s poses is computationally challenging and yields inconsistent results. This research suggests a manipulator-route-tracking method employing deep-reinforcement-learning techniques to deal with this problem. The method of this paper takes an end-to-end-learning approach for closed-loop control and eliminates the process of obtaining the inverse answer by converting the path-tracking task into a sequence-decision issue. This paper first explores the feasibility of deep reinforcement learning in tracking the path of the manipulator. After verifying the feasibility, the path tracking of the multi-degree-of-freedom (multi-DOF) manipulator was performed by combining the maximum-entropy deep-reinforcement-learning algorithm. The experimental findings demonstrate that the approach performs well in manipulator-path tracking, avoids the need for an inverse kinematic solution and a dynamics model, and is capable of performing manipulator-tracking control in continuous space. As a result, this paper proposes that the method presented is of great significance for research on manipulator-path tracking.
Nonlinearizad of Fast Fourier Transform

A unified mathematical form of reversible nonlinear transformations based on a nonlinear tensor product is presented in the form of fast algorithms. The main goal of this article is to show that almost all Fourier transforms (FFTs) can be both generalized and non-linear. Nonlinearity and generalization of the FFT are based on two recursive rules, which generate nonlinear transformations using a fast algorithm. For each rule, simple relations indicate the number of elementary nonlinear operations required by the fast algorithm. The resulting scheme is formed in three stages. The first step involves the so-called 2×2 Basic Non-Linear Transforms (BNLT). The second step is based on sparse nonlinear transformations (SNLTs), which are direct sums of BNLTs. The third step is Fast Nonlinear Transform (FNLT) as an SNLTS overlay product.
A Privacy Robust Aggregation Method Based on Federated Learning in the IoT

Federated learning has been widely applied because it enables a large number of IoT devices to conduct collaborative training while maintaining private data localization. However, the security risks and threats faced by federated learning in IoT applications are becoming increasingly prominent. Except for direct data leakage, there is also a need to face threats that attackers interpret gradients and infer private information. This paper proposes a Privacy Robust Aggregation Based on Federated Learning (PBA), which can be applied to multiple server scenarios. PBA filters outliers by using the approximate Euclidean distance calculated from binary sequences and the 3σ criterion. Then, this paper provides correctness analysis and computational complexity analysis on the aggregation process of PBA. Moreover, the performance of PBA is evaluated concerning ensuring privacy and robustness in this paper. The results indicate that PBA can resist Byzantine attacks and a state-of-the-art privacy inference, which means that PBA can ensure privacy and robustness.
Measurements and the Arrow of Time

Geoscientists often say that surface seismic amplitudes are ‘small’ to support superposition in the analysis of combinations of waves. In surface seismic projects the source might be dynamite or very large vibrators to shake the Earth. So, we should be sceptical that such amplitudes are ‘small’. Machine learning is currently very popular and many want to use plausible data from machine learning methods in place of measurements or example seismic data. I think that practising geophysicists should prefer to use the laws of physics with measurements in Inversion schemes generally labelled FWI (Full Waveform Inversion). This method generates synthetic seismograms from a proposed Earth Model, analyses the differences between the measurements and the synthetic and tries to converge on a best Earth Model. In such a scheme measured data is far more valuable than plausible projected values.
Identifying Stylometric Characteristics of Domain Specific Texts Using Classification Algorithms: A Study of Library Science Articles published in 2020

Academic writing has played an essential role in communicating the cognitive aspects of the human mind. Natural Language Processing (NLP) tools enable us to examine linguistic knowledge. However, writing patterns and applicable linguistic characteristics differ geographically. The study's primary purpose is to understand the global writing pattern and linguistic diversities of research articles in the LIS domain. The corpus was identified from four SCOPUS-enrolled open-access libraries and information science journals. The journals published in India and outside India were selected for the study in 2020. The syntactic complexity in 147 text documents was measured using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TASSAC). The corpus was further examined using the Structural Equation Model (SEM) to determine the causal relationship among independent variables such as syntax features and readability scores. The results depict the differences in the patterning of syntactic features at both the global and national levels. Furthermore, the study allows us to see how linguistic diversity is underplayed in research writings and helps to understand writing patterns through cross-country comparisons. Furthermore, the paper employs model-based reasoning to identify global and national latent variables.
A Study: How AI is Incorporated in the Middle East Banking

The banking sector has undergone significant transformation due to the swift advancements in Artificial Intelligence (AI). The present study elucidates the benefits of aforementioned technology and their influence on both individuals and communities. The paper discusses a novel cohort of banking institutions that have reaped the rewards of advancements in AI-powered electronic gadgets and internet-based applications. For example, mobile devices are increasingly utilized to conduct transactions via economic institutions' accounts. author has discussed in the paper that advancements in AI have brought significant benefits to the banking sector. The adoption of AI-powered electronic gadgets and internet-based applications has revolutionized the way banking services are delivered, providing convenience, personalized experiences, and enhanced customer service. AI technologies have also contributed to fraud detection and prevention, improved operational efficiency, and facilitated financial inclusion. As the banking sector continues to evolve, the integration of AI is likely to play an increasingly vital role in shaping the future of banking services.
Enhanced transformer model for video caption generation

Abstract Automatic Video captioning system is a method of describing the content in a video by analysing its visual aspects with regard to space and time and producing a meaningful caption that explains the video. A decade of research in this area has resulted in a steep growth in the quality and appropriateness of the generated caption compared with the expected result. The research has been driven from the very basic method to most advanced transformer method. Machine generated caption of a video must be adhering to many expected standards. For humans, this task may be a trivial one, however its not the same for a machine to analyse the content and generate a semantically coherent description for it. The caption which is generated in a natural language must also adhere to its lexical and syntactical structure. The video captioning process is a culmination of computer vision and natural language processing tasks. Commencing with template based conventional approach, it has surpassed statistical method, traditional deep learning approaches and is now in the trend of using transformers. This work made an extensive study of the literature and has proposed an improved transformer‐based architecture for video captioning process. The transformer architecture made use of an encoder and decoder model that has two and three sublayers respectively. Multi‐head self attention and cross attention are part of the model which bring about very beneficial results. The decoder is auto‐regressive and uses a masked layer to prevent the model from foreseeing future words in the caption. An enhanced encoder‐decoder Transformer model with CNN for feature extraction has been used in our work. This model captures the long‐range dependencies and temporal relationships more effectively. The model has been evaluated with benchmark datasets and compared with state‐of‐the‐art methods and found to be slightly better in the performance. The performance scores are slightly varying for BLEU, METEOR, ROUGE and CIDEr. Furthermore, we propose the idea of curriculum learning if incorporated can improve the results again.
CNN stability training improves robustness to scanner and IHC-based image variability for epithelium segmentation in cervical histology

In digital pathology, image properties such as color, brightness, contrast and blurriness may vary based on the scanner and sample preparation. Convolutional Neural Networks (CNNs) are sensitive to these variations and may underperform on images from a different domain than the one used for training. Robustness to these image property variations is required to enable the use of deep learning in clinical practice and large scale clinical research.CNN Stability Training (CST) is proposed and evaluated as a method to increase CNN robustness to scanner and Immunohistochemistry (IHC)-based image variability.CST was applied to segment epithelium in immunohistological cervical Whole Slide Images (WSIs). CST randomly distorts input tiles and factors the difference between the CNN prediction for the original and distorted inputs within the loss function. CNNs were trained using 114 p16-stained WSIs from the same scanner, and evaluated on 6 WSI test sets, each with 23 to 24 WSIs of the same tissue but different scanner/IHC combinations. Relative robustness (rAUC) was measured as the difference between the AUC on the training domain test set (i.e., baseline test set) and the remaining test sets.Across all test sets, The AUC of CST models outperformed "No CST" models (AUC: 0.940-0.989 vs. 0.905-0.986, p < 1e - 8), and obtained an improved robustness (rAUC: [-0.038, -0.003] vs. [-0.081, -0.002]). At a WSI level, CST models showed an increase in performance in 124 of the 142 WSIs. CST models also outperformed models trained with random on-the-fly data augmentation (DA) in all test sets ([0.002, 0.021], p < 1e-6).CST offers a path to improve CNN performance without the need for more data and allows customizing distortions to specific use cases. A python implementation of CST is publicly available at https://github.com/TIGACenter/CST_v1.
FORMATION OF THE FOUNDATIONS OF THE RUSSIAN STATEHOOD IN HIGHER EDUCATION INSTITUTIONS

The article describes the systemic complex construction of a new compulsory course in the humanities in Russian universities "Fundamentals of Russian Statehood". The importance of discipline for Russian students in modern international conditions is emphasized. The importance of a thorough and thoughtful approach to teaching students in order to realize the significance and value of the state is substantiated. The actual tasks facing universities at the first stage of the reform implementation are determined and ways of their solution are proposed
INCREASING THE EFFICIENCY OF THE USE OF SOLAR PANELS

A method for controlling a two-axis solar tracker using an astronomical algorithm and a microcontroller is proposed. A practical solution is considered to achieve maximum efficiency of solar panels, which is associated with calculating the position of the sun, determining the desired an-gle for the solar tracker, reading the current angle of the tracker and adjusting its position
DEVELOPMENT OF COGNITIVE AND CREATIVE ABILITIES OF STUDENTS ON THE EXAMPLE OF THE OPTIONAL COURSE «EXPERIMENTAL FOUNDATIONS OF ELEMENTARY PHYSICS»

The problem of the success of teaching schoolchildren is posed. The possibilities of developing creative and research abilities of students are considered on the example of the developed program of the optional course "Experimental foundations of elementary physics". The purpose of the course, tasks are described. The composition of the course and its content are described. The data of observations as a result of the implementation of the optional course program are presented
MODEL OF SOLAR RADIATION INTENSITY ON A VERTICAL SURFACE

The paper considers a method for compiling a model of the intensity of direct and scattered solar radiation on vertical surfaces of arbitrary orientation
A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.
INDIVIDUAL APPROACH TO LEARNING UNIVERSITY STUDENTS

The article substantiates the need to strengthen the individual approach in the educational process in modern conditions. Directions for the organization of the educational process, taking into account the individual approach in the course of teaching disciplines at the university, which contribute to improving the quality of educational services, are identified
LEGAL SELF-EDUCATION OF YOUTH STUDENTS

The article discusses the features of the process of legal self-education. The methods of self-education of legal culture of students are generalized. The role of teachers of higher education in the legal self-education of students is determined and possible directions of their participation in this process are proposed
Knowledge and skills extraction from the job requirements texts

The analysis of the requirements for vacancies in the labor market shows that they are multi-level language constructions of several words with complex semantic relationships. The aim of the research is to develop a method for extracting short texts of knowledge and skills from texts of job requirements that have a complex organizational structure. The method consists in supplementing the structure of complex sentences with new relationships by means of a BERT neural network model trained on the texts of online vacancies and moving from a complex text to a set of simple word combinations. The process of additional training (finetunig) of BERT neural network models from the Sberbank AI laboratory on the texts of online vacancies is shown. Two mechanisms for adding new links between requirements words, taking into account knowledge from the subject area, are implemented: linear and through the addition of the parsing tree. In the course of the experiment, a comparative analysis was carried out for several combinations of the listed tools. The combination that showed the best result was 'the BERT + deeppavlov_syntax_parser model + a linear method of adding links'. The applicability of the method was demonstrated on the text corpus of online job requirements. The proposed method has shown higher efficiency than the rule-based approach, which involves the use of formal rules and grammar rules for natural language analysis. Using the method will allow you to quickly identify the key changes in the needs of the labor market at the level of requirements texts of individual knowledge and skills.
Using HurtLex and Best-Worst Scaling to Develop ERIS

ERIS, a lexical resource of Modern Greek for offensive language detection, is the result of cleansing, enriching and assigning graded offensiveness values to the EL branch of HurtLex. ERIS contains 1148 entries and is openly available. Graded values were obtained with the Best-Worst Scaling methodthat was applied with the Litescale tool. Nouns and adjectives that have humans as a target were found to attract bigger offensiveness values. The classification of the terms in ERIS with the BWS method and a previous classification of a substantial subset of these terms into “offensive (context in/dependent)” with the inter-annotator agreement method are found to stand in a broad correlation, thus validating the methodology that was adopted to produce a more fine-grained and informative affective lexical resource. ERIS contains 1148 terms and their inflectional paradigms. It is openly available under the CC-BY-NC 4.0 license.
Linear Data Structures and Their Applications

In a linear data structure, the elements are arranged in a specific order. Each element on a linear data structure is connected to the elements that proceed it as well as the element that follows it. In other words, the elements are placed in sequential order. Hence, it is possible to traverse the elements in sequential order in a single pass. The objective of the chapter is to provide a basic understanding of linear data structures and their implementation in Python. The chapter is organized as follows. The chapter starts with an introduction to the linear data structure. The objective is to provide a basic understanding of linear data structures. A comparison of linear data structures and non-linear data structures is given next. Next, all of the above-mentioned data structures are explained in brief. The Python implementation of all of these data structures is provided. After that, their applications and their implementation in Python is discussed. The chapter concludes by highlighting the essential concepts.