Reviewerly/exHarmony huggingface dataset:
DBVinci – towards the usage of GPT engine for processing SQL Queries

One of the goals of Natural Language Processing (NLP) is transforming sentences to output relevant information in a given context. For instance, relevant applications such as chatbots, translation systems, and sentiment analysis classifiers work that way. The advance of NLP techniques made it possible to automate complex tasks, such as converting text queries to tabular data queries, specifically SQL, to return contextualized data. Since it is crucial in many areas to interpret the data to obtain information and consider the particularities of a text-to-SQL parser, we propose a SQL processing engine whose internals are customized with natural language instructions. DBVinci is our proposed processing model which is based on OpenAI’s GPT-3.5 Text-davinci-003 engine that can perform language tasks such as text-to-SQL, consistent instruction-following, and supports inserting completions within text. Our framework is on top of GPT-3.5 and decomposes complex SQL queries into a series of simple processing steps, described in natural language. DBVinci outperforms well-known text-to-SQL methods (e.g., RAT-SQL and SQLOVA) reaching 89.7% of execution accuracy, considering WikiSQL benchmark. We also obtain impressive performance without the need of large scale annotated dataset for fine-tuning the downstream task, by achieving 90% accuracy in zero-shot setting. Therefore, we conclude that to obtain competitive results using the Pre-trained Language Model (PLM), there is no need of the “pre-training+fine-tuning” paradigm, besides that, when employing zero-shot in the proposed method, we can achieve promising results.
Beyond Imitation: Exploring Novelty in Generative AI

This research paper presents a comprehensive review of recent advancements in Generative Artificial Intelligence (AI). We survey key developments in generative models, including GANs, VAEs, and Transformers, highlighting their applications in diverse domains such as image synthesis, text generation, and natural language processing. We also explore emerging trends in ethical AI, interpretability, and robustness, emphasizing the need for responsible AI development. Our analysis provides insights into the current state of Generative AI, paving the way for future research directions and ethical considerations in this rapidly evolving field.
Online Museums as a Complex Work of Art

Since the 21st century, digitalization has been actively implemented in all spheres of life, and this trend is relevant for Russian museums. The main content of the museum's digital environment is characterized by visual diversity and various communication accents. The same theme or exhibitions can be exploited in various visual forms, which allows you to see different shows. An important component of visual content is its split-screen implementation, which partially restores the lost material architectonics to the abstract digital space, or rather, creates an illusion of such a process. The purpose of this article is to analyze the digital museum as a complex art object. As a result of the study, it is concluded that the key features of the subject are media integration, the variability of communication modes, game manipulation of exhibiting objects, and in some cases overacted conceptuality. Communication with the digital museum’s content includes elements of interactivity, virtual immersion in the environment. All of these are characterized by a mostly private tone. By all its activities in the virtual media environment, the museum demonstrates the desire to correspond to modernity, to be an integral element of today's public culture, to have a memorable image.
[Hematopoietic system formation during embryogenesis].

Hematopoietic cells are a group of cells that first appear in the midembryonic stage of the mouse and are essential for body growth and maintenance. For a long time, their development was widely assumed to be divided into primitive and definitive hematopoiesis. However, erythromyeloid progenitors were identified as the wave between primitive and definitive hematopoiesis, and at least three waves were recognized. An even more multilayered structure of hematopoietic development is becoming evident in recent years, with the progress and spread of lineage tracing experiments. This review will focus on recent advances in the behavior of hematopoietic stem and progenitor cells in the embryo revealed by cell lineage-tracking experiments.
Transient Attacks Against the VMG-KLJN Secure Key Exchanger

The security vulnerability of the Vadai, Mingesz, and Gingl (VMG) Kirchhoff–Law–Johnson–Noise (KLJN) key exchanger, as presented in the publication “Nature, Science Report 5 (2015) 13653”, has been exposed to transient attacks. Recently an effective defense protocol was introduced (Appl. Phys. Lett. 122 (2023) 143503) to counteract mean-square voltage-based (or mean-square current-based) transient attacks targeted at the ideal KLJN framework. In this study, this same mitigation methodology has been employed to fortify the security of the VMG-KLJN key exchanger. It is worth noting that the protective measures need to be separately implemented for the HL and LH scenarios. This conceptual framework is corroborated through computer simulations, demonstrating that the application of this defensive technique substantially mitigates information leakage to a point of insignificance.
TRANSLATION IN THE FIELD OF HUMANITIES IN BULGARIA: SPECIFICITIES, CHALLENGES AND PERSPECTIVES

Translation in the field of humanities, which plays a key role in the evolution of ideas and thought on an international scale, is characterised by inherent specificities, and poses many difficulties. The article summarizes and exposes its particularities and carries an overview of its place in Bulgaria today, relying on statistical data, based on which it derives and analyses several trends. The subsequent analysis of the existing imbalances and some relatively unfavourable trends in the translation of humanities in Bulgaria takes into account the objective market circumstances, focuses more on the human factor and especially on the central figure of the translator, and points out as an important problem the practical lack of theory and criticism of the translation in the field of humanities. A multidisciplinary approach is suggested to support this highly specialised translation, as well as specific actions at all stages of the transfer of scientific knowledge through translation, with an explicit emphasis on translators’ training and the development of adequate criticism of translations. The practical implementation of this approach could form the basis of a long-term strategy to support and develop the translation in the field of humanities in Bulgaria. The article combines an analytical approach with an overview and programmatic presentation.
Corpus Generation to Develop Amharic Morphological Segmenter

Morphological segmenter is an important component in Amharic natural language processing systems. Despite this fact, Amharic lacks large amount of morphologically segmented corpus. Large amount of corpus is often a requirement to develop neural network-based language technologies. This paper presents an alternative method to generate large amount of morph-segmented corpus for Amharic language. First, a relatively small (138,400 words) morphologically annotated Amharic seed-corpus is manually prepared. The annotation enables to identify preﬁxes, stem, and sufﬁxes of a given word. Second, a supervised approach is used to create a conditional random ﬁeld-based seed-model (on the seed-corpus). Applying the seed-model (an unsupervised technique on a large unsegmented raw Amharic words) for prediction, a large corpus size (3,777,283) of segmented words are automatically generated. Third, the newly generated corpus is used to train an Amharic morphological segmenter (based on a supervised neural sequence-to-sequence (seq2seq) approach using character embeddings). Using the seq2seq method, an F-score of 98.65% was measured. Results show an agreement with previous efforts for Arabic language. The work presented here has profound implications for future studies of Ethiopian language technologies and may one day help solve the problem of the digital-divide between resource-rich and under-resourced languages.
Features of designing and conducting an online course in business English for adult learners

Foreign language communication skills are crucial for today’s business professionals, as they provide opportunities for advancing their career and conducting efficient communication with partners world-wide. The present article and the underlying research explore the implementation of andragogical principles in an online course for Business English. One of the most important principles entails providing conditions for comfortable learning, including, above all, the format of interaction, which ultimately affects its efficiency and organization. The relevance of distance learning is confirmed by current national laws, as well as by social studies that indicate a demand for online classes in Russia. The use of digital technology for the development of adult learners’ foreign language communication allows them to be educated in a setting that simulates their working environment. This would suggest that when organizing adult education, it is recommended to rely on the learners’ professional experience, offer practice-oriented tasks, encourage autonomy and offer authentic, problem-oriented materials in order to facilitate interaction and set off discussions. With all the advantages of the distance learning format, adult learners may, however, encounter difficulties which are to be taken into account. This article presents an online course for Business English, taking into consideration the identified principles of andragogy, specific properties of workplace interaction and particular features of the distance learning format. The research findings may be of practical value for designing and organizing a course in Business English with its methodology based on adult learning theory principles.
Comparison of Machine Learning Algorithms for Crime Prediction in Dubai

This study aims to find the most accurate algorithm that is capable of predicting crimes in Dubai. It compares models on a dataset of sample crimes in the Emirate of Dubai, United Arab Emirates using the open-source data mining software WEKA, which enabled us to use Random Forest, KNN, SVM, ANN, Naïve Bayes and Decision Tree, We chose those algorithms as former studies that were effective used them. We have applied the algorithms on a dataset containing 13440 Major Crime in four categories occurred between 2014 and 2018. After comparing the models and analyzing their success rates, we identified the ideal algorithms and evaluated the effectiveness of variables in making predictions by measuring the correlation coefficients. One of the study's most crucial recommendations is to increase the variables and data, also adding more details about the crime, the criminal, and the victim. These variables make an impact on the analysis and the ultimate prediction.
Specifics of Secondary Vocational Education as a Stage in the Process of Professional Student Self-Determination

The paper proposes a sociological approach to the analysis of the process of professional self-determination of students studying under secondary vocational education programs in colleges affiliated with a HEI. Profes-sional self-determination is considered as a prolonged process of professional self-actualization, self-realization and professional development as part of the entire educational and professional-labor “biography” of the subject. The article defines the main factors influencing the choice of applicants in favor of college edu-cation, presents the analysis of the results of sociological research of the process of professional self-determination of college students of Nizhny Novgorod Institute of Management – branch of Russian Presiden-tial Academy of National Economy and Public Administration and College of Economics and Law of Dzerzhin-sky branch of Russian Presidential Academy of National Economy and Public Administration. College enroll-ment reasons are highlighted, as well as groups of SPE students depending on their future plans for profes-sional self-actualization, self-actualization and professional development.
An in Depth Analysis of Blockchain Technology, and its Potential Industrial Applications

The emergence of blockchain technology represents a significant advancement in the field of computer science. Blockchain, an innovative technology that functions as a decentralized and publicly accessible record of all financial transactions, has significantly transformed the manner in which commercial activities are conducted. Companies and large- scale technology corporations have started substantial investments in the blockchain industry, a sector that experts forecast will exceed a valuation of $3 trillion during the next five-year period. The surge in its popularity may be ascribed to its robust security measures and comprehensive resolution for all issues pertaining to digital identity. The system in question is a decentralized digital ledger. A blockchain refers to an immutable and decentralized ledger composed of blocks, which function as collections of entries. The interconnection among these blocks is secured using encryption. The blockchain technology is captivating due to its inherent qualities, and it has significant potential in several domains owing to its desired attributes such as decentralization, transparency, and irreversibility. While blockchain technology is now most prominently associated with cryptocurrency, it has a diverse array of potential applications. This article aims to explore the many applications of blockchain in the domains of voting mechanisms, Internet of Things (IoT), supply chains, and identity management.
An Automated Partial Derivative Based Method for Detecting and Monitoring Moving Objects

This work proposes a method for detecting and tracking moving objects that rely onthe partial differential equation technique and can track both forward and backward. In order to reduce the amount of noise in the output video, it is first divided into many frames and then pre-processed using methods for the Gaussian filters. The transfer function is calculated on the binarized frames following the acquisition of the absolute difference for forward tracking and backward tracking. The forward and backward tracking outputs are combined at the object tracking step to get the desired outcome. Statistics like f-measure, accuracy, retention, and precision are used to evaluate the predicted technique, and classic motion detection methods are also used to examine its effectiveness. According to the evaluation results, the suggested system is superior to the usual high-accuracy rate techniques.
ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

The high pace rising global competitions across education sector has forced institutions to enhance aforesaid aspects, which require assessing students or related stakeholders’ perception and opinion towards the learning materials, courses, learning methods or pedagogies, etc. To achieve it, the use of reviews by students can of paramount significance; yet, annotating student’s opinion over huge heterogenous and unstructured data remains a tedious task. Though, the artificial intelligence (AI) and natural language processing (NLP) techniques can play decisive role; yet the conventional unsupervised lexicon, corpus-based solutions, and machine learning and/or deep driven approaches are found limited due to the different issues like class-imbalance, lack of contextual details, lack of long-term dependency, convergence, local minima etc. The aforesaid challenges can be severe over large inputs in Big Data ecosystems. In this reference, this paper proposed an outlier resilient semantic featuring deep driven sentiment analysis model (ORDSAENet) for educational domain sentiment annotations. To address data heterogeneity and unstructured-ness over unpredictable digital media, the ORDSAENet applies varied pre-processing methods including missing value removal, Unicode normalization, Emoji and Website link removal, removal of the words with numeric values, punctuations removal, lower case conversion, stop-word removal, lemmatization, and tokenization. Moreover, it applies a text size-constrained criteria to remove outlier texts from the input and hence improve ROI-specific learning for accurate annotation. The tokenized data was processed for Word2Vec assisted continuous bag-of-words (CBOW) semantic embedding followed by synthetic minority over-sampling with edited nearest neighbor (SMOTE-ENN) resampling. The resampled embedding matrix was then processed for Bi-LSTM feature extraction and learning that retains both local as well as contextual features to achieve efficient learning and classification. Executing ORDSAENet model over educational review dataset encompassing both qualitative reviews as well as quantitative ratings for the online courses, revealed that the proposed approach achieves average sentiment annotation accuracy, precision, recall, and F-Measure of 95.87%, 95.26%, 95.06% and 95.15%, respectively, which is higher than the LSTM driven standalone feature learning solutions and other state-of-arts. The overall simulation results and allied inferences confirm robustness of the ORDSAENet model towards real-time educational sentiment annotation solution.
A Formal Ontology for Special Needs Conditions

Ontology deals with the conceptualization of domain concepts and the relationships among them while special needs conditions refer to any condition that requires assistance ranging from disability to giftedness and talentedness. Unified categorization of special needs conditions and the availability of comprehensive facts and knowledge about each category of special needs conditions have become major problems in the field of special education. It is, therefore, necessary to develop an ontology for special needs conditions that will unify the knowledge and fact about special needs conditions to support various decision-making in the special education domain. Hence, this research builds a formal ontology for special needs conditions that unifies the major categories of special needs conditions which are disability and giftedness, and talentedness, and includes various assistive technology that is applicable to each nature of special needs conditions. The knowledge engineering method was used to build the ontology while the language of formalization is Description logic. Web ontology language in protégé was used to implement the work and evaluated with competency questions in form of simple queries. This ontology can be adopted in all special education domains to forestall the problem of information overloading and inconsistency. Keywords: Special needs conditions, Assistive technology, Ontology, Protégé. CISDI Journal Reference Format Adebayo, E.O., Ayorinde, I.T &amp; Akinkunmi, B.O. (2023): A Formal Ontology for Special Needs Conditions. Computing, Information Systems, Development Informatics Journal. Vol 14 No 2, Pp 31-60. Available online at https://www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V14N2P4xx
Improvement of management mechanisms for the development of educational organizations of the Tula region

The article examines the current state of management of educational organizations and the educational process in the Tula region, analyzes the results of a study on improving the management mechanisms for the development of educational organizations in the region, offers a number of recommendations for improving the management mechanisms for the development of educational organizations in the region.
Text encryption with singular values

In this paper, we will talk about how text is encrypted. Generally, this is done through a number of processes that spread out the later values in a matrix, which is A through remittances are linear and the use of svd enters, including one key where work will start on the application process. New matrices B1 and B2 of the original text matrix are generated in a compound, and the key is entered. The second step of the dispersion involves utilizing SVD on the final matrices and switching the S matrix between the two spectra for the new matrices C1 and C2, which signifies inserting the matrix of the encoded text as correlative F = [C1 C2]. To determine the accuracy of the work, we calculated the matrix of the absolute value of the difference between the matrix of numbers for the original text characters lettering before encryption and the matrix of numbers for text characters after decryption, which resulted in a zero matrix, as well as the calculation of the average values of this matrix which resulted in a zero matrix.
A Formal Ontology for Special Needs Conditions

Ontology deals with the conceptualization of domain concepts and the relationships among them while special needs conditions refer to any condition that requires assistance ranging from disability to giftedness and talentedness. Unified categorization of special needs conditions and the availability of comprehensive facts and knowledge about each category of special needs conditions have become major problems in the field of special education. It is, therefore, necessary to develop an ontology for special needs conditions that will unify the knowledge and fact about special needs conditions to support various decision-making in the special education domain. Hence, this research builds a formal ontology for special needs conditions that unifies the major categories of special needs conditions which are disability and giftedness, and talentedness, and includes various assistive technology that is applicable to each nature of special needs conditions. The knowledge engineering method was used to build the ontology while the language of formalization is Description logic. Web ontology language in protégé was used to implement the work and evaluated with competency questions in form of simple queries. This ontology can be adopted in all special education domains to forestall the problem of information overloading and inconsistency. Keywords: Special needs conditions, Assistive technology, Ontology, Protégé. CISDI Journal Reference Format Adebayo, E.O., Ayorinde, I.T &amp; Akinkunmi, B.O. (2023): A Formal Ontology for Special Needs Conditions. Computing, Information Systems, Development Informatics Journal. Vol 14 No 2, Pp 31-60. Available online at https://www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V14N2P4x
Optimized Deep Networks Structure to Improve the Accuracy of estimator algorithm in Deep Networks learning

An optimization algorithm based on training and learning is formed based on the process of training and learning in a class. A deep neural network is one of the types of feedforward neural networks whose connection pattern among its neurons is inspired by the visual cortex of animals' brain. The present study considers decreasing prediction error for the types of time series and the uncertainty in estimation parameters, improving the structure of the deep neural network and increasing response speed in the proposed neural network method; besides, the competitive performance and the collaboration among the neurons of deep neural network are also increased. Selected data is related to Qeshm weather (suitable weather conditions to study our purpose) prediction during 2016 onwards. In this study, for the purpose of analyzing the prediction issue of power consumption of domestic expenses in the indefinite and severe fluctuation mode, we decided to combine two methods of Long Short-Term Memory and Convolutional Neural Network. For the training of the deep network, the BP algorithm is used.