Reviewerly/exHarmony huggingface dataset:
STUDYING THE INFLUENCE OF THE USE OF AN ELECTRONIC TEXTBOOK ON THE CREATIVE ABILITIES OF STUDENTS IN «BIOLOGY» LESSONS

This article deals with the study of the influence of the use of an electronic textbook for schoolchildren in the lessons of «Biology» on the creative abilities of students. It is shown that in general, the use of an electronic textbook contributes to the increase of cognitive interest in the subject, the growth of students academic performance in the subject.
Contextual-cognitive convolutional attentive LSTM approach for sentiment analysis of Tamil review

Movie goers use Twitter and other websites to share their thoughts and feelings regarding Tamil films in this age of social networking and online interaction. It is essential to examine these feelings for realizing the responses of the public and assess a film's level of success. In this research, a novel contextual-cognitive convolutional attentive long/short-term memory (C3-ALSTM) strategy for assessing Tamil movie reviews via Tamil tweets is presented. We first gather the Tamil movie review dataset through an open database to assess the suggested C3-ALSTM approach. We normalize the gathered data by eliminating extraneous information, noise and redundant data. Tokenization and the removal of stop words and special characters are employed in this phase. Following data cleansing, term frequency-inverse document frequency (TF-IDF) is applied for extracting the attributes. The suggested approach evaluates the Tamil reviews based on these retrieved features. The suggested approach is put into practice using the Python platform and its accuracy, precision, recall and f-measure metrics are examined. According to the research's findings, the suggested method outperforms other used techniques for sentiment assessment of Tamil reviews. In addition, this research attempts to offer insightful information about how the public reacts to particular Tamil films, advancing knowledge of public preferences and attitudes toward the Tamil cinema industry.
Navigating the Algorithmic Marketplace: How AI is Changing Consumer Psychology and Brand Loyalty

The rise of artificial intelligence (AI) has fundamentally reshaped the online marketplace. AI algorithms now curate content, personalize recommendations, and influence consumer decision-making in profound ways. This paper explores the impact of AI on consumer psychology and brand loyalty. We examine how AI algorithms exploit psychological biases to nudge consumer behavior and cultivate brand preference. Additionally, we investigate how AI can be used ethically to build trust and foster long-term brand relationships not only changing the dynamic in the tech world but also making an Impact in the Market and other areas. This paper focuses on how AI is capturing the Market and how AI is contributing to Markets and Business growth.
Analysis of the Conditions of Access and Use of ICT and LKT in Remote Teaching and their Impact in the Linguistic Aspect in First-Year Students of the Bachelor’s Degree of English Teaching at Universidad Nacional, Coto Campus, in 2021

This qualitative research paper explores the reality that freshman students lived in the Bachelor’s Degree in English Teaching at Universidad Nacional, Coto Campus, Brunca Region, during remote-teaching classes in 2021 with regard to accessibility, competence, and the importance of Information and Communication Technologies (ICTs) and Learning and Knowledge Technologies (LKTs). The information gathered shows that the competence in using these tools was very poor due to the students’ not having enough knowledge at the beginning of the semester. Also, the access to ICTs and LKTs was limited because of different factors that tend to happen in rural areas; therefore, it is necessary to work on trainings and the accessibility to these devices in the Brunca Region. However, the ICTs and LKTs were important to the learning and teaching process of these students because the results show that without these tools, the access to the lessons would not have been possible nor the development of the four linguistic skills of the English language.
Science and Technology Study Tools for Education on Digitality and Digital Technologies

This essay offers several tools from various science and technology studies (STS) branches, for the education on and with digital technologies and digitality. Emblematic STS works that have researched the digital are first discussed, followed by a brief review of the main theories and concepts that have been applied for this purpose. The paper is then concluded with a synthesis of relevant STS understandings to study the digital, in addition to a set of teaching elements for its education and some applicable lessons for a critical pedagogy on this issue.
Implementation of the meaning-formimg and organizing functions of the texture in the piano works of modern Ukrainian composers

Statement of the problem. Recent research and publications. Today the texture of a music work is not only a marker of an epochal style, but also, more than ever, of an individual style. Studying the principles of textural organization, understanding the specifics of the action of textural functions gives the performer the keys to deciphering the artistic intent of the work, the system of the composer’s style and finding the corresponding performance techniques. And in the conditions of the complex genre system of modern music, the texture is able to carry out the function of an interpretative guide to the heart of the composer’s idea, the author’s concept. The scientific works by such musicologists as H. Vinohradov (1977), I. Denysenko (2010), H. Ihnatchenko (1984), R. Kashyrtsev (2021), V. Moskalenko, V. Prykhodko (1997), H. Savchenko (2023), I. Tsurkanenko (1998), M. Chernyavska (2015a; 2015b), S. Shyp (1998), S. Shkoliarenko (2017) are devoted to the study of musical texture. A panoramic understanding of the development vectors of the doctrine of texture in the works of Ukrainian scientists can be found in the article by O. Hnatyshyn (2015). Objectives, methods, and novelty of the research. The purpose of the article is to reveal the ways of realizing the meaning-forming function of the texture based on the analysis of piano works by modern Ukrainian composers: Serhii Piliutykov’s “Fairy Tale” and Zoltan Almashi’s “Elegy”. The stylistic, genre, systematic and functional methods of researching are used in the article. For the first time in Ukrainian musicology, the selected works are analyzed from the point of view of textural organization. Results and conclusion. The situation of the gap the connection between the genre and the texture actualizes the meaning-forming and organizing function of the texture. S. Piliutykov in “Fairy Tale” builds a meaningful multilayered structure horizontally and vertically, working with different stylistic thematic elements, thereby revealing the programmatic idea. As a result, a specific musical narrative is created, which is unfolded in the interaction of vertical and horizontal texture layers. In “Elegy” by Z. Almashi, the principles of textural organization chosen by the composer seem to push the boundaries of the genre, provide for a different timbre embodiment, and timbre-texture transformations become decisive in building the composition in time and space.
Technological imagination as a source of the culture of neural networks

Abstract The study represents a partial output of research on the culture of artificial neural networks, as the authors call the cultural complex, in which a number of different actants participate (technologies, their users, results of the generation process, their recipients, media, etc.) and which is constituted by language games that have a performative function. The aim of this study is to conduct a media-archaeological insight into the imaginative layer of these language games and to point out that one of the sources of neural network culture is precisely the deeply historically anchored technological imagination. The genealogy of this imagination is traced in the study from its ancient origins to the 1950s, when the idea of the artificial mind was transformed into a scientific theorem and founded the research field of artificial intelligence. In this way, the paper draws attention to the fact that when we think and talk about artificial intelligence, we are talking about a set of imaginations that should not be confused with reality, but rather treated as technological fictions.
Internet of things application in digital marketing to improve the efficacy of a company

The expansion of digital marketing at the international level has been so fast and the benefits associated with it are clearly increasing day by day that developing countries are strongly encouraged to use it and each of them has specific programs to implement it. After telecommunication networks of computer, Internet and mobile phone, Internet of things (IOT) is considered as the third development in the global information industry, which has attracted the attention of many organizations and customers. This technology, relying on the intelligentization of objects and the use of internet platforms, is able to create wide and diverse services in e-commerce. This is while the demands of users for more diverse and better-quality services through the Internet are increasing. In this paper, application of IOT in digital marketing has been introduced and the effects of using it to increase efficiency and create innovation in the company have been discussed. The statistical sample in this research included all the managers of small and medium companies in Baghdad, which was determined as the statistical sample size of 275 based on the Krejcie Morgan table. Data analysis was done by the method of structural rates, which was used in this article by the method of least squares. The results showed that the use of IOT and electronic commerce had a positive and significant impact on the performance of companies (financial, internal processes, customers, development and innovation). The results of this research show that IOT technology has provided the basis for improving performance and providing better services in digital marketing by creating an integrated and strong database of suppliers, manufacturers, distributors, customers and products. Also, in the last part of the article, the implementation framework of this technology in e-commerce for the use of the country’s managers in various industries is presented.
Analysis of Student-Related Social Stressors among School Teachers

Teachers who have day-to-day relations with students are exposed to mistreatment by students; therefore, they are daily exposed to social stress, which affects their moods. The purpose of the research was to identify the most perceived student-related social stressors (SSS) contributing to the social stress of school teachers in elementary and high schools. Moreover, to explore their relationship to the positive and negative teachers' affect. An online survey was employed for data collection by using publicly available addresses of elementary and high schools in Slovenia. The results show that the most often perceived SSS are disliked students and disproportionate student expectations in both elementary and high schools; a statistically significant relationship between SSS and teachers' affect was found. The principals of elementary and high schools should pay attention to maximizing teachers' competencies to improve their classroom management and enable the teachers to learn about cognitive strategies for moderating mood effects on judgements about social events.
Models and algorithms for optimizing the location of solar batteries

The study aimed to create a method for optimizing the position of solar batteries with fixed location to increase their efficiency, namely, the generation of maximum energy during a certain time, as well as reducing the unevenness of electricity generation over time. The study allowed developing a method of optimizing the position of solar batteries to increase their efficiency, namely obtaining the maximum electrical energy generation both with a completely fixed location of solar batteries and with a seasonal change in position. The developed models and algorithms, the given examples and the results of their solution with the construction of polynomial regressions and their graphical representation, integration of polynomials to obtain the values of the generated energy give a clear idea of the application of the method of optimizing the position of solar panels to maximize the generated energy and equalize the generation characteristics over time, as well as ways to develop them further. The theoretical research made it possible to solve an important scientific and technical problem of increasing the performance of solar batteries by placing them in an optimal position relative to the Sun. The model of placement of the solar battery and the method that allows optimizing its position were further developed. The field of using the Matlab computer program for simulating the operation of solar batteries was further expanded. For the first time, the work offers a model and method of using solar cells with different positions relative to the Sun designed to increase the amount of electric energy generated and equalize the energy generation characteristic, which is expressed as a dependence of power over time. For this purpose, the authors introduce the coefficient of unevenness of the generation characteristic, which establishes the relationship between the maximum power and the capacities at other moments of time and allows objectively evaluating the unevenness of the characteristic. The practical value of the proposed solutions consists in the ability to simulate the operation of the solar battery in various conditions based on experimental studies and using the Matlab computer system.
Implementasi Metode Marr-Hilldert Untuk Mendeteksi Batas Wilayah Gempa

he development of technology is currently increasing significantly and has become a dependent tool for humans around the world because the role of technology can help solve problems that are difficult for humans to solve. One form of positive role from technological developments is to detect the edges of digital images. Indonesia is one of the countries with the highest potential for earthquakes in the world, the cause of Indonesia is earthquake prone because it has many volcanoes not only from the Pacific Ring of Fire. There is still the Alpide Belt which is the most active earthquake pathway in the world, which is also influenced by the occurrence of earthquakes in Indonesia. In addition, there are also piles of three continental plates, namely the Indonesia-Australia plate from the south, Eurasia from the north, and the Pacific plate from the east which adds to the hustle and bustle of the geological structure in this country. Therefore, the need for information but detecting the boundaries of the earthquake area is very much needed to be able to minimize the negative impacts that occur, especially casualties, so far this information is still lacking
Implementasi Metode Marr-Hilderth Operator Untuk Mendeteksi Batas Tepi Pantai

Changes in the shape of the waterfront greatly impact the tourism business, which causes a reduction in domestic and foreign visitors to visit. One way to make it easier to obtain information about changes in the shoreline is to utilize the information contained in a coastal image. Edge detection is the first step of image segmentation to obtain information in an image. The edge contains from points that have a difference with the others. This study describes how to apply the Marr-Hilderth Operator method to detect image edges. Edge detection is the initial process to obtain information in images and characterize object boundaries, then edges can be used in the process of segmentation and identification of objects in images. The benefit of the detection of coastal boundaries is carried out to provide information to tourist area entrepreneurs regarding changes in the shape of the beach, so that the effects can be minimized as early as possible and make it easier for tourism object managers to obtain information about changes in the shape of the beach.
Study on the Twoing Criterion with Pre-pruning and Bagging Method for Dispersed Data

In the paper, the issues related to classification based on dispersed data are considered. Dispersed data's idea is to be able to effectively make use of data collected independently from different information systems/sources/units on a single topic in the development of a classification model that could classify a new object irrespective of the information systems/source/unit the object is from. As in Federated learning approaches, also here, data is protected and not shared between the owners. Local models are built using the bagging method and decision trees with the Twoing criterion. Only prediction vectors generated based on the local models are sent to central server. Final aggregation is done using majority voting. The main purpose of the paper is to study the quality of classification obtained with the proposed approach. Another goal is to investigate the impact of the pre-pruning tree process on the quality of classification. Moreover, the comparison of results obtained for the Twoing criterion and for the Gini index during the tree construction is presented. The experiments were performed on seventeen dispersed data sets, two of which reflect the natural dispersion that occurs in reality – dispersed medical data collected by different hospitals and dispersed medical data collected in different countries. The contribution of this paper is to observe the effectiveness of using Twoing criteria as a splitting criterion together with bagging method in development of classification model for data stored in independent dispersed sources.
Learning Decision Rules from Sets of Decision Trees

This paper is devoted to the study of the problems of learning inner and general decision rules that are true for the maximum number of decision trees from a given set. Inner rules correspond to paths in decision trees from the root to terminal nodes. General rules are arbitrary rules that use attributes from the considered decision trees. We propose a polynomial time algorithm for the optimization of inner rules, show that the problem of optimization of general rules is NP-hard, and describe a heuristic for this problem. We compare the considered algorithm and heuristic experimentally on artificially generated datasets and induced from them decision trees with Gini index as a splitting criterion.
Semantic Bridging between Conceptual Modeling Standards and Agile Software Projects Conceptualizations

Software engineering benefitted from modeling standards (e.g. UML, BPMN), but Agile Software Project Management tends to marginalize most forms of documentation including diagrammatic modeling, focusing instead on the tracking of a project's backlog and related issues. Limited means are available for annotating Jira items with diagrams, however not on a granular and semantically traceable level. Business processes tend to get lost on the way between process analysis (if any) and backlog items; UML design decisions are often disconnected from the issue tracking environment. This paper proposes domain-specific conceptual modeling to obtain a diagrammatic view on a Jira project, motivated by past conceptualizations of the agile paradigm while also offering basic interoperability with Jira to switch between environments and views. The underlying conceptualization extends conceptual modeling languages (BPMN, UML) with an agile project management perspective to enrich contextual traceability of a project's elements while ensuring that data structures handled by Jira can be captured and exposed to Jira if needed. Therefore, concepts underlying the typical software development project management are integrated with established modeling concepts and tailored (with metamodeling means) for the domain-specificity of agile project management. A Design Science approach was pursued to develop a "modeling method" artifact, resulting in a domain-specific modeling tool for software project managers that want to augment agile practices and enrich issue annotation.
Police Interventions as a Context-aware System. A Case of a Contextual Data Modelling

Smart systems which operate in Intelligent Environments (IE) are complex. They analyse the large volumes of various contextual data on-line and often in real time to obtain, autonomously and reliably, the required pro-activeness of a system which operates pervasively. We proposed both a development framework for context-aware systems and a context-based decision making scheme for the system of managing police interventions, focusing on providing support for police patrols in life threatening situations. This system, owing to the symultaneous collection of rich contextual information from many police officers, which constitute the mobile network, as well as the complex processes of contextual reasoning, takes automatic decisions on supporting officers in emergency. We implemented the initial, yet not trivial, simulations of the system behaviour within the whole city. The results obtained prove the feasibility of the framework.
Topic Classification for Short Texts

In the context of TV and social media surveillance, constructing models to automate topic identification of short texts is key task. This paper formalizes the topic classification as a top-K multinomial classification problem and constructs worth-to-consider models for practical usage. We describe the full data processing pipeline, discussing about dataset selection, text preprocessing, feature extraction, model selection and learning, including hyperparameter optimization. When computing time and resources are limited, we show that a classical model like SVM performs as well as an advanced deep neural network, but with shorter model training time.
Performance of Multi-Clustering Recommender System after Selection of Clusters based on V-Measures

Identification of neighbourhood based on multi-clusters has been successfully applied to recommender systems, increasing recommendation accuracy and eliminating divergence related to a difference in clustering schemes. The algorithm M-CCF was developed for this purpose that was described in author's previous papers. However, the solution do not equally take advantage on all the partitionings. Selection of clusters to forward to recommender system's input, without deterioration in recommendation accuracy, can simplify its structure. The article describes a solution of a cluster selection based on entropy measure between clustering schemes, eliminating ones, which are redundant. The results reported in this paper confirmed its positive impact on the M-CCF system's overall recommendation performance (measured by RMSE and Coverage).
Data Privacy Re-visited During Covid19

The COVID19 Pandemic has highlighted our dependence on online services (from government, e-commerce/retail, and entertainment), often hosted over external cloud computing infrastructure. The users of these services interact with a web interface rather than the larger distributed service provisioning chain that can involve an interlinked group of cloud providers. The data and identity of users are often provided to service provider who may share it (or have automatic sharing agreement) with backend services (such as advertising and analytics). We propose the development of compliance-aware cloud application engineering, which is able to improve transparency of personal data use -- particularly with reference to the European GDPR regulation. Key compliance operations and the perceived implementation challenges for the realization of these operations in current cloud infrastructure are outlined. This talk will also explore how the convenience-vs-privacy challenges can be realised as users and service providers go on-line, and the economics behind delivering privacy services as part of cloud-based provision.