Reviewerly/exHarmony huggingface dataset:
The role of information communication technologies on African indigenous knowledge systems: Folktales

African Indigenous Knowledge Systems (AIKS) are values that were passed among African generations by word of mouth. Despite the Fourth Industrial Revolution's (4IR) facilitation of rapid communication for every aspect of society, many Africans have not embraced it to store AIKS, mainly due to concerns over biases and prejudices embedded in aspects like information and communication technologies (ICTs). Using decoloniality theory, this paper aims to show the importance of AIKS, which faces extinction due to the combined effects of the deaths of members of the old generation and acculturation. In the methodology section, Afrocentricity, which celebrates rootedness in African culture and history, was used together with storytelling as a means to examine the perceptions of Afrocentric scholars as revealed in their folktale works that were analyzed qualitatively. Results revealed that lack of exposure to ICTs is one of the main reasons for the scant AIKS virtual literature. Many African people do not have access to other needs like food, education, clothing, electricity, and shelter, which effectively renders ICTs ‘secondary, yet access to information and participation are fundamental rights. We urge stakeholders to implement policies that promote the preservation of AIKS in electronic repositories. Memory devices and other ICT accessories should be responsive to African cultures. Investment in ICT infrastructure and training, reliable electricity, and addressing all the basics are critical.
The Role of Typological Features of Students in Conducting Collective Training Sessions

When conducting the educational process in any educational institution, due attention is not paid to the individual characteristics of a single student (cadet), including the level of intellectual development, the speed of perception, the depth of assimilation of educational material in the learning process, etc .The article considers a methodological approach to the evaluation of the educational process, to the methods of its adaptation and management for a significant increase in efficiency the process of training specialists, for example in the field of information security, especially when switching to a multi-level university system of training specialists, taking into account the requirements that are constantly changing when planning the educational process.The article reveals the actual problem of conducting collective training sessions taking into account the individual typological characteristics of students to improve the quality of training specialists.The key element of the learning stage is the psychological type of the learning personality, its basic psychodynamic characteristics.
Relationship Between Types of Family Education and Emotional-volitional Sphere in Student Age

Intra-family relations are the main component of the educational potential of the family. In our society, they are conditioned by state and domestic relations. Their significance in the formation and development of the individual is due to the fact that they are the central link in social relations in which the individual enters into the process of his personal growth.The family is the main phenomenon that accompanies a person throughout his life. Its influence on the personality, its complexity and versatility give rise to many approaches to the study of the family and definitions that can be found in the scientific literature. The object of study is the family as a social institution, a small group or a system of relationships.The purpose of the presented pilot psychological study was to study the influence of types of family education on the formation and development of the emotional-volitional component in student age.The paper studies such characteristics as student age, emotional-volitional sphere, types of family education, family, emotions and will.In the work, a bibliometric analysis of the research problem was carried out, an experiment was carried out. Thanks to the psychological experiment, the types of family education and their influence on the development of the emotional-volitional sphere of such a category as students were determined.The results of the study are based on a systemic, axiological, humanistically oriented methodological component. The methods of pilot psychological research were experiment, observation, survey, method of statistical processing of results.
Quasistatic Computation and Thermodynamic Approach

Some of the processes that we observe in our day to life are not reversible. It is more interesting to observe that irreversible processes are not equally difficult in both directions. A practical example is aging—which is obvious in the forward direction but very difficult to reverse. The cause of reversibility can be classified under two types—involvement of dissipative force and non-quasistatic processes. While processes involving dissipative force can’t be made reversible, reversibility due to non-quasistatic processes can be made reversible by sacrificing time. In computational theory, some problems are also difficult to solve compared to others. Difficulty in solving a problem is mainly for two reasons—a. due to loop (undecidable) and b. nondeterminism (NP-problem). There is a similarity in the sense that—a. both irreversible processes involving dissipative force and undecidable problems can’t be converted to reversible process/solved by deterministic Turing machine and b. both to make non-quasistatic processes reversible and to solve NP-problem by deterministic machine; time increases. Also, this paper describes an analogy between NP/P-problem and the irreversible/reversible problem and introduces a new formulation of Turing machines, i.e., quasistatic computation, to establish the relation.
CapsFraud: A Deep Method to Identify Fraudulent Transaction in Blockchain Using Capsule Network

The fraud cases are increased in different sectors, especially in financial sector day by day with advancement of latest technologies. Financial data are confidential; therefore, the organizations are concerned about privacy and they do not want to disclose the financial data outside for research purpose. The reliability and simplicity of transactions in blockchain make it popular for financial transaction. In spite of that blockchain allows many abnormal schemes in smart contract which in turn causes adverse effect in financial transaction. Therefore, many schemes have been proposed with machine learning techniques to avoid anomaly in blockchain-based transaction. In spite of that, machine learning technique can not able to detect relevant features, which causes abnormal behavior in financial transaction. Therefore, in this proposed work, a deep model has been incorporated with blockchain to identify the fraudulent transaction. The results reveal that the proposed method can recognize illicit transactions with gratifying performance.
CoDescribe: An Intelligent Code Analyst for Enhancing Productivity and Software Quality

Automating the documentation, explanation, and modification of source code has been an area of intense research interest for several decades. This paper proposes a novel framework for analyzing source codes to generate natural language summaries, answer complex user queries by reasoning, and even support syntactic error checking and code conversion to other languages. The framework incorporates transformer-based models and knowledge graphs to understand the context of the code and the user’s queries. The contributions of this proposed approach represent a significant leap forward by overcoming the limitations of the most advanced ChatGPT model. The framework ensures the accuracy and reliability of the responses while handling multiple source code files efficiently, making it a valuable asset for software developers and stakeholders.
A Novel Video Encryption Scheme Using Giant Sudoku for Secured Data Transmission

Sudoku is an interesting puzzle game whose popularity increases day by day not only as a game but also for its application in various fields of research. Different types of Sudoku puzzles are there based on their grid size. In this paper, we have developed a novel method for securing Video files using Giant Sudoku. Solving Sudoku is a difficult task, and finding one particular solution amongst multiple solutions is quite impossible. The solution space of a Giant Sudoku of size $$25\times 25$$ is very large. We have generated the key by combining Giant Sudoku with a time stamp for Video Encryption. During key transmission, we are passing the signature of our key using the Sudoku-hash function for authentication. This method protects the secured giant cipher key using Sudoku-hash which provides a secured and authenticated encryption process. Our proposed method can resist brute force attacks, phishing attacks, man-in-the-middle attacks, dictionary attacks, and credential stuffing attacks in an efficient and effective way.
Digital Misogyny Unveiled: An Examination of Online Hate Speech Targeting Women in Bangladesh

Discriminatory and sexist language expresses bias towards men and projects that women are inferior around the world. It is also evident in the patriarchal society of Bangladesh. While they are often prevalent subtly in communication taking place in person, the anonymous nature of the internet has enabled people to be vile towards women in the virtual world. Online hate speech and misogynistic behavior have become pervasive issues in today's digital landscape. Most people take Islamic verses literally or believe the religious clerics without checking their words' authenticity. The socio-religious idea of women being subservient to men produces numerous hate speeches against women on social media platforms. The presence of numerous online religious lectures spewing offensive words against women made the situation worse in Bangladesh. They conform to the prevalent idea that men should control women's agency, and for that purpose, even violence is acceptable. This article aims to provide a comprehensive analysis of the phenomenon, specifically focusing on the context of Bangladesh. Through case studies, it looks into how religious leaders fuel online hatred towards women.It further explores the various forms, impacts, and underlying factors of misogynistic online speech. By delving into the complexities of this issue, it also discusses the challenges faced by women in seeking redress and proposes recommendations to mitigate online violence against women in Bangladesh.
Syntactic annotation for Portuguese corpora: standards, parsers, and search interfaces

In the last two decades, four Portuguese syntactically annotated corpora were built along the lines initially defined for the Penn Parsed Historical Corpora (Santorini, 2016). They cover the old, the middle, the classical and the modern periods of European Portuguese, as well as the nineteenth and twentieth century Brazilian Portuguese, and include different textual genres and oral discourse excerpts. Together they provide a fundamental resource for the study of variation and change in Portuguese. In the last years, an effort was made to maximally unify the annotation scheme applied to those corpora, in such a way that the searches done on one corpus could be done in exactly the same manner on the others. This effort resulted in the Portuguese Syntactic Annotation Manual (Magro & Galves, 2019). In this paper, we present the syntactic annotation for the Portuguese Corpora. We describe the functioning of ParsPort, a rule-based parser which makes use of the revision mode of the query language Corpus Search (Randall, 2005–2015). We argue that ParsPort is more efficient to our annotation efforts than the probabilistic parser developed by Bikel (2004), previously used for the syntactic annotation of the Portuguese Corpora. Finally we mention recent advances towards more user-friendly tools for syntactic searches.
Compression and Reconstruction of Magnetotelluric Data Based on Convolutional Neural Network

Abstract Magnetotelluric method is one of the important geophysical methods, and its signal acquisition requires more stacking times and longer stacking time. With the development of instruments, the acquisition time becomes longer and the amount of data becomes larger, which brings new challenges to data storage and transmission. Aiming at the above problems, a compression and reconstruction technology of magnetotelluric time series based on convolutional neural network is proposed. This paper introduces two convolutional autoencoders based on convolutional neural networks, which can effectively compress data space, improve transmission efficiency, and have high data reconstruction accuracy. Using the measured magnetotelluric time series, two autoencoder models based on convolutional networks are verified in this paper, which proves the feasibility of convolutional autoencoders in magnetotelluric data compression; the results show that model 2 can better reproduce It constructs the magnetotelluric time series, and has high training efficiency and good generalization ability.
Research and Application of Microseismic First Arrival Picking Method Based on Geophone Spacing Constraint

Abstract Picking the first arrival of microseismic events has been widely recognized as an essential step for data processing of microseismic monitoring. In this paper, we have provided a practical method, which was developed by introducing geophone spacing constraint to original cross-correlation functions for further optimization of seismic arrival picking. First, all valuable traces are processed by a cross-correlation algorithm and time difference correction. Then, based on distance constraints between geophones, local cross-correlation is applied to manipulate those acquired records for further time difference correction and multi-trace records after the second time difference correction are superimposed to form stacking traces, followed by picking the arrivals of microseismic events using the STA/LTA method. Finally, by combining the stacking of first arrival information and the relative correction of time difference, the first arrival time of microseismic events can be obtained. The actual data processing results show that, compared with the conventional multi-trace cross-correlation method, our proposed method reasonably avoids the influence of the offset on the first arrival time, thus effectively improving the accuracy of the first arrival picking of microseismic events.
Deep Learning for Inversion of Tipper Data of a Certain Railway Tunnel in Tibet Area

Abstract The traditional inversion methods for tipper rely excessively on the selection of the initial model, whose global search capability is poor. Inspired by the significant approximation advantage of deep learning for nonlinear inverse problems with big data, we design a deep learning architecture called TipInv-net for the inversion of tipper. TipInv-net takes the improved U-net as the basic framework to obtain the tipper response characteristics of the abnormal body, and then, dense skip connection is applied among the nested standard convolution modules to alleviate the gradient disappearance problem and enhance feature propagation. It’s worth noting that we construct a feature pyramid of tipper response via average pooling to obtain multi-scale receptive fields, which, thus enhancing global and detail location of abnormal body. The theoretical model test indicates that the position and attitude of geological anomaly body can be distinguished via TipInv-net, even in the presence of a certain level of noise, the inversion accuracy will not be greatly affected. TipInv-net has strong generalization. Besides, after the training process of the network, we can obtain the inversion result immediately. In order to verify the effectiveness of the method in this paper, the inversion of the field tipper data of a section of a certain railway tunnel in Tibet area is carried out.
Construction and Significance of Comprehensive Geophysical Observatory Array in Dabie Monitoring and Forecasting Experimental Site

Abstract This paper reviews the development process of the seismic scientific experimental site at home and abroad and introduces the background, significance and construction of the the Dabie Monitoring and Prediction Experimental Site, which led by Anhui Earthquake Agency. During the 12th Five-Year Plan, the office of this experimental site was built, which is located in Lu’an Seismic Station. During the 13th Five-Year Plan, a comprehensive geophysical observation array is built, including a seismic science array, a flux-gate array, a optical-fiber array, a infra-sound array and a baseline of the gravity field at this experimental site. Through these constructions, it can not only play a important role in the study of earthquake monitoring and prediction, but also contribute to the study of the crustal structure detection, crustal strain analysis, seismic acoustic characteristics and gravity equipment calibration.
Methodological foundations of educational organizations site quality assessment (for example Vladivostok State University)

Currently, higher education institutions are focused on promoting their image on the Internet in order to occupy a certain niche there for presenting information about themselves. Thanks to the possibilities of the information Internet environment, universities contribute to improving the quality of the educational process. An important aspect in the formation of competitive advantages over other educational institutions is the quality of educational web resources. The distribution of scientific and educational streams is regulated by regulatory requirements that have a huge impact on the formation of the quality of websites of higher educational institutions. The important role of the monitoring process of assessing the quality of educational institutions' websites for the formation of recommendations based on the evaluation results according to the proposed methodology is revealed. The methodology is based on the theory of stakeholders, where groups of interested persons are considered by the fact of the existence of the studied object, indirectly or directly influencing it. A nomenclature of quality indicators has been developed based on the allocation of important criteria for each group, in this work these are students, parents, applicants and employees. The identification of indicators and values of their satisfaction serves as a foundation for the subsequent assessment of the quality of university websites according to the proposed methodology. The methodology includes several stages that accurately track all indicators during its use. The verification of the quality assessment algorithm was carried out on the example of the website of the Vladivostok State University. The data obtained are systematized and calculated according to the proposed formula, taking into account all interested groups. The results are of practical interest for a systematic assessment of the quality of educational institutions' websites and can be offered for use.
Operational shock: A method for estimating cyber security incident costs for large Australian healthcare providers

This paper introduces a novel cyber incident cost estimation methodology, applicable to large Australian healthcare providers. A review demonstrates the poor utility of current risk estimation approaches and the vulnerability of healthcare networks is evaluated using Leibniz's law of indiscernibles, and Evans' theory of vague objects. Finally, a quantitative cost calculation method is proposed, merging temporal and impact variables with service data from the Australian Institute of Health and Welfare.
Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks

The slowdown of Moore's law and the existence of the "von Neumann bottleneck" has led to electronic-based computing systems under von Neumann's architecture being unable to meet the fast-growing demand for artificial intelligence computing. However, all-optical diffractive neural networks provide a possible solution to this challenge. They can outperform conventional silicon-based electronic neural networks due to the significantly higher speed of the propagation of optical signals (≈108 m.s-1) compared to electrical signals (≈105 m.s-1), their parallelism in nature, and their low power consumption. The integrated diffractive deep neural network (ID2NN) uses an on-chip fully passive photonic approach to achieve the functionality of neural networks (matrix-vector operations) and can be fabricated via the CMOS process, which is technologically more amenable to implementing an artificial intelligence processor. In this paper, we present a detailed design framework for the integrated diffractive deep neural network and corresponding silicon-on-insulator integration implementation through Python-based simulations. The performance of our proposed ID2NN was evaluated by solving image classification problems using the MNIST dataset.
Towards Blockchain-Integrated Enterprise Resource Planning: A Pre-Implementation Guide

In the face of numerous challenges in supply chain management, new technologies are being implemented to overcome obstacles and improve overall performance. Among these technologies, blockchain, a part of the distributed ledger family, offers several advantages when integrated with ERP systems, such as transparency, traceability, and data security. However, blockchain remains a novel, complex, and costly technology. The purpose of this paper is to guide decision-makers in determining whether integrating blockchain technology with ERP systems is appropriate during the pre-implementation phase. This paper focuses on the literature reviews, theories, and expert opinions to achieve its objectives. It first provides an overview of blockchain technology, then discusses its potential benefits to the supply chain, and finally proposes a framework to assist decision-makers in determining whether blockchain meets the needs of their consortium and whether this integration aligns with available resources. The results highlight the complexity of blockchain, the importance of detailed and in-depth research in deciding whether to integrate blockchain technology into ERP systems, and future research prospects. The findings of this article also present the critical decisions to be made prior to the implementation of blockchain, in the event that decision-makers choose to proceed with blockchain integration. The findings of this article augment the existing literature and can be applied in real-world contexts by stakeholders involved in blockchain integration projects with ERP systems.