Reviewerly/exHarmony huggingface dataset:
Partial Domain Bearing Fault Diagnosis Method Based on Weighted Bi-Classifier

The investigation of bearing fault diagnosis is a crucial and pressing core technology in the advancement of rotating mechanical equipment, as well as a significant national strategic requirement. In the realm of fault diagnosis, domain adaptation techniques have been extensively employed to effectively address the challenge of aligning source and target domains with dissimilar data distributions. Nevertheless, prevailing approaches typically presume that the source and target domains possess identical health conditions. However, in industrial applications, the target domain frequently constitutes a subset of the source domain. In light of this issue, this research paper presents a novel unsupervised partial domain fault diagnosis approach. Firstly, it incorporates a hybrid convolutional block, which is a lightweight network structure, to enhance the receptive field through wide kernel convolution and extract global information from various states. Secondly, it employs a dual classifier structure and incorporates an adaptive source domain class weighting method to dynamically select the most pertinent classes for target domain prediction. Additionally, an enhanced local maximum mean square discrepancy is utilized: A proposed approach is presented to attain alignment, and the experimental analysis of multiple datasets under varying working conditions demonstrates that this method exhibits a high level of recognition accuracy when dealing with asymmetry within certain domain classes.
The Role of Extended Reality in Tourism

The purpose of this study is to examine the attainable opportunities that Virtual Reality (VR) technologies can offer for the tourism services sector to provide higher level of consumer experience for their customers and in addition how to generate business advantage and opportunity for higher profitability based on effective implementation. Using the method of comparative case study analysis of three chosen touristic destinations as primary research, this paper analyses the several aspects of customer experience that can be presented with or without the active using of possibilities offered by VR- and AR-technologies. Starting from the theory of cognitive info communication, in this paper we examine the possibilities and effects of VR solutions implemented in tourism services sector in the light of customer experience.
Diffusion Probabilistic Model Based Face Anonymization in Embedded Environments

Computer vision systems depend on vast and diverse datasets to achieve optimal performance. However, in scenarios where data collection involves personal identifiable information, privacy-protecting legislation can impede the acquisition process. To address this challenge, we present a novel face anonymization pipeline capable of running on embedded platforms. The proposed methodology combines denoising diffusion probabilistic models (DDPMs) and advanced image super-resolution techniques, such as DFDNet and ESPCN, to achieve high-quality anonymized facial images while preserving crucial facial keypoint locations. We conduct a comprehensive evaluation of the pipeline's performance, encompassing image quality, keypoint precision, and efficiency on two popular single-board computers, namely the Nvidia Jetson Nano and the Raspberry Pi 4B. The source code is available at https://github.com/balazsmorv/facediffusion.
(Pre-)Linguistic Problem Solving Based on Dynamic Semantics

Based on the famous monkey-banana problem, we propose a general solution mechanism for alloplastic coping using dynamic and database semantics, dynamic programming and minimalist grammar as a pre-linguistic device. We show that the generation of a solution plan (i.e. a program) for a given problem - “fetch the banana from the ceiling” - involves solving the frame problem of artificial intelligence in terms of generalization and unification operators, where the problem space is represented by locally interpreted feature-value relations. Using dynamic programming for the backward planning, a sequence of mentally represented instructions is obtained under the constraint that antecedent and consequent states of successive actions must be compatible to each other. These conditions can be naturally represented by the feature-matching mechanism of minimalist grammars. We discuss the crucial difference of this kind of pre-linguistic inference with proper linguistic derivations.
Uncovering Financial Fraud: The Vital Role of Forensic Accounting and Auditing in Modern Business Practice

Abstract This article explores the critical roles of forensic accounting and auditing in modern business, focusing on uncovering financial irregularities and promoting transparency. Through a meticulous literature review and analysis, the study emphasizes the significance of these practices. The purpose is to underscore their importance, methodologies, and implications. Methodologically, the article employs a comprehensive literature review and meticulous analysis of real-world applications. It investigates cases of corporate fraud, financial statement analysis, and dispute resolution. Ethical and legal considerations are highlighted, emphasizing the adherence to professional codes and standards. The study reveals that forensic practices are pivotal in detecting financial misconduct, upholding financial transparency, and ensuring accountability. Technological advances, such as data analytics and AI, enhance efficiency and accuracy. The research establishes the need for continuous development and professional education to tackle the evolving financial challenges. In summary, this article highlights the central roles of forensic accounting and auditing in modern business, their methodologies, and ethical considerations. The study underscores their importance in detecting financial fraud, promoting transparency, and fostering trust.
A Study on Password Manager: Users' Perspective

Passwords are a crucial aspect of online security, but many people struggle to create and remember strong passwords for different accounts. The use of passwords has become an integral part of our daily lives, with many individuals having multiple accounts for various services. The challenge of keeping track of numerous passwords has led to the development of password managers, which provide a secure and convenient way to manage passwords. Password managers are software applications that store and manage passwords for various online accounts. This study aims to investigate the usage and effectiveness of password managers by conducting a survey through online questionnaire on 300 individuals to gather data on their password management practices.
Credit Card Fraud Detection Using Machine Learning Algorithms

It is crucial for credit card firms to be aware of credit card sales that are fraudulent so they can stop charging customers for goods they did not purchase. The credit card detection issue involves modelling previous credit card transactions using the information from these that were later determined to be fraudulent. Afterwards, in a new transaction is analyzed using this model to determine whether it is fraudulent or not. Our goal is to completely identify fraudulent transactions while reducing erroneous fraud categories. The goal in this is to study which is to demonstrate how modelling data sets are utilized in machine learning to detect credit card fraud. Both the theoretical and application portions of this article have been covered.
Analysis of Various Machine Learning Classifiers for Prediction and Classification of Breast Cancer

Everyone is impacted by cancer, the most common disease in the world. The classification of cancerous cell types is one utilization of intelligent retrieval in medical science. To increase the survival rate, it is essential to identify breast cancer early. To accurately detect and anticipate breast cancer in its early stages, a sophisticated framework is therefore required. Using machine learning classification techniques, the article evaluates the model's prediction power. The accuracy, recall, support, fl-score and AUC-ROC curve metrics were used to assess the classification performance of the Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), LR, K-Nearest Neighbors (KNN), Bagging, and Boosting Classifiers. According to the results, Support Vector (SVM), Bagging and Adaboost classifiers, Random Forest were successful in achieving the best accuracy of 96.00%.
Analysis of the Security Features of Banknotes to Facilitate Authentication

The main motto of this research is to develop a secure and reliable method for authenticating notes in a digital format. The proposed system uses a combination of processing methods along with machine learning algorithms for analysing the pre-defined parameters of a digital note. The parameters are analysed and compared to existing digital notes for authentication. Additionally, the system also examines the texture, shape, and colour of the note to further enhance the security of the authentication. The proposed system provides significant improvements in the accuracy and speed of digital note authentication as compared to existing methods.
Performance Analysis of Machine Learning Algorithms for Password Strength Check

Passwords are a vital component of system security, providing a simple, direct means of protecting a system and representing the identity of an individual for a system. However, the same patterns that people use to create passwords also render them vulnerable to attack. Since the bulk of records has weak passwords or well-known password patterns, password cracking algorithms have been developed to anticipate the password both offline and online. In order To prevent these vulnerabilities from being exploited, it is critical for companies to realize the risks that passwords provide and to create robust policies governing the selection and use of passwords. The proposed model employs multiple Machine Learning (ML) methods, including Decision Tree (DT), Logistic Regression (LR), Random Forest Classifier (RF), and K Nearest Neighbor, pushing users to select a strong password as a protection against these online and offline attacks. The Random Forest Classifier had the best results during the testing of the models over the test set, with an accuracy of 98%, and Logistic Regression achieved the lowest accuracy of 82%.
PROBLEM-BASED APPROACH TO TEACHING UKRAINIAN SYNTAX IN SECONDARY SCHOOL

Background. The problem-based learning presented in the study follows the 3C3R bilateral design model which is conceptually grounded in two types of components: three core Cs addressing the context, content, and connections of the learning problem (context, content, connection), and three action-oriented Rs involving research, reasoning, and reflection. These elements support cognitive processes for problem-solving skills and self-directed learning (Hung, 2009). Purpose. The article aims to develop an innovative lesson based on the 3C3R model of bilateral design for problem-based learning, conduct experimental instruction in the 8th-grades of basic secondary school, and report the results of problem-based teaching a range of syntactic phenomena of the Ukrainian syntax such as apposition, comparative constructions, and subordinate comparative sentences. Method. Theoretically, the research relies on the works of Ansarian and Teoh (2018), Yavir and Bilousenko (1992), and Kucheruk (2009). Additionally, it incorporates the use of the bilateral design model proposed by Hung (2006). The 28 participants of the experimental training involved eighth-grade students from Lyceum No. 2 in the Mogilev branch of the Hnivan City Council, (n=13), and Lyceum No. 4 in Berdychiv (n=15). The data were collected from students through a survey which aimed to assess the comprehensibility of the topic; evaluate the teacher's instructional clarity; and doing some language tasks. Results and discussion. The study found that teaching syntax through problem-based learning significantly impacted the cognitive and emotional development of the learners. 75% of them confirmed that the presentation of problem-solving tasks was comprehensible, engaging, and clear. Most students grasped linguistic distinctions between the comparative constructions under study. Notably, 82.1% of the students accurately identified these syntactic structures in sentences. Further research perspectives are related to developing a rating scale for assessing the skills and techniques in problem-based learning; creating effective feedback mechanisms, and fostering a dialogue between teachers and students.
TEACHING LITERARY ANALYSIS THROUGH PLOT CONNECTIONS TO EDUCATION MAJORS (BASED ON THE NOVEL IN VERSE "MARUSIA CHURAI" BY LINA KOSTENKO)

Background. A holistic analysis of literary works is a crucial skill for Ukrainian literature teachers. However, analysis methods (sequential, problem-thematic, compositional, etc.) may not provide a comprehensive understanding of a work's uniqueness. Therefore, there is a need for new ways of studying literary texts to consider all their components and represent them as a model of reality. Purpose. The paper's goal is to outline theoretical approaches and practical methods of analyzing a literary work through plot connections (based on Ihor Kachurovsky's approach) in Ukrainian literature classes. The analysis was applied to the novel in verse "Marusya Churay" by Lina Kostenko while doing the course "Analysis of a literary work: literary and methodological aspects" with Secondary Education majors, Taras Shevchenko National University of Kyiv). Methodology. Several methods including general scientific and literary methods, theoretical literary and comparative-historical methods were used to analyze the novel's plot connections, the space and temporal organization, the system of characters and plot dynamics, as well as Lina Kostenko's innovation in modelling the world image. Teaching how to analyze a literary work through plot connections has involved heuristic conversations and debates, introducing theoretical definitions through collective discussions. Teaching plot analysis with a person-oriented approach allows for individual actualization of a literary work based on the recipient's experience, needs, and emotions. Results and discussion. The paper highlights the importance of teaching how to analyze plot connections in literature. It demonstrates how this analysis helps present a literary work as artistic integrity, based on the author's worldview and intentions. The article uses Lina Kostenko's novel as an example to showcase the methodology and practical application of this analysis. The article also outlines ways to involve historical and literary context in the analysis.
Enhancing Content Creation Workflows through Automatic Speech Recognition Techniques

This research provides a comprehensive review of prevailing Automatic Speech Recognition (ASR) methods and their profound impact on media production. Central to this exploration is the pivotal role of the transformer model, showcasing its unique self-attention mechanism ideal for tasks demanding comprehension of temporal relationships. We conduct an in- depth comparison, evaluating leading ASR models like Multilingual Machine Speech (MMS) from Meta, Whisper from OpenAI, and Google's Universal Speech Model (USM). Their performance is gauged against commercial services from tech giants such as Microsoft Azure, Amazon Web Services, and Google Cloud Platform. The paper covers aspects of ASR systems like voice activity detection, language identification, multilanguage support and the metrics used to characterize the accuracy of the systems. The research also identifies important challenges, like the dearth of data for some languages and the intricacies associated with linguistic nuances. Further, we discuss the role of ASR in media production, ranging from generating time-based captions to revolutionizing editing methodologies. Through a detailed breakdown of the ASR process, from audio preprocessing to postprocessing, this study endeavors to merge academic insights with practical applications, empowering media producers to leverage the immense capabilities of contemporary ASR technologies.
Using Knowledge Graphs to Enhance Queries over Heterogeneous Asset Stores

As media organizations accumulate diverse data from various sources, the need for effective data integration and information retrieval becomes increasingly critical. Graph databases offer a flexible and efficient platform for integration by leveraging graph-based data models and querying mechanisms. Graph databases can seamlessly integrate disparate data sets regardless of their original format or structure. Knowledge can be added to the graph through the application of semantic meaning to the data. Knowledge graphs can be enhanced with results of Machine Learning algorithms that extract additional information from the underlying data. This paper will demonstrate how knowledge graphs offer unique capabilities for understanding patterns over collections of large data sets.
WEBQUEST TECHNOLOGY IN COMPUTER LESSONS IN PRIMARY SCHOOL

The article is devoted to webquest technology in computer science lessons in elementary grades. The purpose of the article is to provide a general description of the webquest technology, to study the specifics of its application in elementary school computer science classes, as well as to identify the impact of webquests on the quality of education. The essence of the concepts “webquest” and “webquest technology” has been clarified. The main idea of the webquest is defined, which consists in the active partnership interaction of students with each other and with web resources, which contributes to the development of motivation to study, information literacy and digital competence of schoolchildren. The main elements of webquest technology (scenario, tasks, resources, interactivity, evaluation) are defined. It was found that there are several classifications of webquests, one of the most optimal is the division of webquests by the level of technical complexity. The algorithm for preparing and conducting a webquest has been adapted for computer science lessons in elementary school. It was found that the webquest is built according to the logic of the technology of problem-based learning: from posing a problem to ways of solving it, presenting the result and reflection, which is aimed at the development of the student of education as an active subject of life: Several online resources for webquests are offered, which are advisable to use in lessons computer science in elementary grades (LearningApps.org, Kahoot!, Quizlet, Quizizz, Edpuzzle, Webquest). The topics in computer science for the fourth grade are offered, from which it is advisable to conduct webquests. The main features of the use of webquests in computer science lessons in elementary school are analyzed. Tethodological recommendations for teachers have been developed. It was found that when using webquests in computer science classes, certain challenges and limitations should be taken into account when using webquest technology in the educational process. It was determined that webquest technology is an effective means of developing information literacy and digital competence of junior high school students in computer science lessons in elementary grades.
Inclusive-Exclusive Model Training Framework to Jointly Perform Semantic Segmentation and Uncertainty Map Estimation

In unsupervised or semi-supervised approaches, large quantity of unlabeled datasets are readily available which could be effortlessly used to develop segmentation models. A key factor in the success of the unsupervised approaches is a framework to generate the uncertainty maps for the unlabeled data/samples based on which the performance of the model is enhanced through refining the training dataset. In this light, we set out to propose/develop a model training framework that jointly estimates the segmentation probability map and uncertainty/confidence map to distinguish between accurate and inaccurate segmentation outcomes. The proposed method relies on seven parallel deep learning models trained on the same training dataset using seven loss functions with varying levels of exclusive (conservative) to inclusive (radical) criteria. To this end, seven different cross-entropy loss functions were developed to give different sensitivity (errors) to the voxels to be included in or excluded from the target structures. The average of the probability maps generated by the seven models would serve as a single probability map of the target structure and the voxel-wise standard deviation or variation of the seven probability maps would be regarded as an uncertainty map. The same residual convolutional architecture was used for the seven networks with only different loss functions from the most exclusive (conservative) to the most inclusive (radical). For evaluation of this framework, the kidney dataset from the Decathlon challenge (kidney segmentation from CT images) and BraTS19 dataset for lesion segmentation from MR images were employed. The visual inspection of the segmentation outcomes (either kidney from CT images or lesion from MR images) revealed the superior performance of the proposed training framework to the conventional approach with a single cross-entropy loss function. The Dice indices for kidney and lesion segmentation improved from 91.7±1.6 to 94.4±1.4 and 90.9±1.8 to 93.1±1.4, respectively, when using the inclusive-exclusive framework. Moreover, a high correlation was observed between the true labels and the voxel-wise uncertainty map obtained from the proposed framework. The estimated uncertainty map could be efficiently used to discriminate between accurate and inaccurate segmentation outcomes. Overall, the quantitative analysis demonstrated the overall improved segmentation performance using the proposed framework as well as the high correlation between the estimated uncertainty indices and the true labels.