Reviewerly/exHarmony huggingface dataset:
DISTANCE EDUCATION IN UKRAINE: CURRENT DEVELOPMENT PROSPECTS

The article develops the theoretical and methodological foundations of distance learning in the systemof continuous education and its characteristic features (flexibility, modularity, parallelism, long-range action, № 7(21) 2023 218 asynchrony, mass character, economic efficiency, the changed role of the teacher, the new role and activity of the student, specialized control of the learning process, the use of modern teachingaids, sociality, internationality); the differences between distance learning and the traditional full-time form are established (in the distance learning system, the student is the customer of knowledge; the information and educational environment of distance learning is much wider, but less than traditional full-time education, regulates the behavior of participants in the pedagogical process and disciplines students to a greater extent); the specifics of the personal interaction of participants in the distance learning process and the management of distance learning processes (human studies, psychophysical, socio-psychological aspects predominate in it) are revealed; carrying out educationalwork in the conditions of remote betrothal puts forward the axiological approach in the first place; resolved a set of issues related to the introduction of a distance learning system into real pedagogical practice (its social, worldview, value, methodological, legal, financial, economic, organizational, didactic, technological, psychological, applied and educational aspects); organizational forms of distance learning are defined (traditional (correspondence); with fragmentary use of information and communication technologies; electronic; combined).
Artificial Attendance System with Two-Way Verification using QR Code Scanning and Face Recognition with Eye Blink Detection

The usage of biometric technology has grown in popularity recently across several industries, including attendance management systems.Utilising biometric technology has been shown to be a more dependable and secure technique of managing attendance.This study suggests an artificial attendance system that uses face recognition with eye blink detection, QR code scanning, and two-way verification.The objective of this system is to enhance the security, accuracy, and reliability of attendance management in various organisations.The suggested system manages attendance via two-way verification.The system first reads a unique QR code that is produced for each user.Second, the system uses face recognition with eye blink detection to confirm the user's identification.The presence and identity of the user are confirmed by the two-way verification.The system also keeps track of the user's attendance's date, time, and place.Deep learning techniques are the foundation of the facial recognition system employed in this study.The system extracts facial features from the user's face using a convolution neural network (CNN).For precise person recognition, the CNN is trained on a big collection of facial photos.Deep learning algorithms are also the foundation of the eye blink detection system.To recognise eye blinks, the system makes use of a long short-term memory (LSTM) neural network.A dataset of facial photos with labelled eye blink data is used to train the LSTM.The Open CV library and Python programming language were used to create the suggested system.A collection of 1000 facial photos with labelled eye blink data was used to test the system.Eye blink detection accuracy for the system was 95% and facial recognition accuracy was 98%.To increase the precision and dependability of attendance management, the proposed system can be implemented in a variety of organisations, such as businesses, schools, and universities.The system may also aid in lowering the time and effort needed for managing attendance and enhancing the security of attendance records
Improving Marketing Performance: How Business Analytics contribute to Digital Marketing

This research is aimed to assess the business analytics impact on marketing performance with mediating role of digital marketing. In order to evaluate the variables, the data was empirically collected from e-commerce industry UAE. 116 responses were included for statistical analysis. The quantitative nature of research enabled to measure the data using SmartPLS 4.0. PLS- SEM applied to assess the research model and validity of the proposed model. The findings revealed a significant association between business analytics and marketing performance whereas, the indirect link between business analytics has significant relevance using digital marketing (mobile Marketing, Social Media Marketing). As a result, business analytics has become an essential tool for companies to improve their marketing performance. By analyzing customer data and market trends, businesses can gain insights into consumer behavior and preferences, allowing them to make informed decisions and target their marketing efforts more effectively. Digital marketing has also emerged as a key component of modern marketing strategies, with its ability to reach and engage with customers through various online channels.
Promises and Problems in the Adoption of Self-Sovereign Identity Management from a Consumer Perspective

Online identification is a common problem but so far resolved unsatisfactorily, as consumers cannot fully control how much data they share and with whom. Self-Sovereign Identity (SSI) technology promises to help by making use of decentralized data repositories as well as advanced cryptographic algorithms and protocols. This paper examines the effects of SSIs on responsible, confident, and vulnerable consumers in order to develop the missing understanding of consumer needs in SSI adoption and define preconditions and necessary considerations for the development of SSI-based platforms and applications.
Are These Descriptions Referring to the Same Entity or Just to Similar Ones?

The Knowledge Graph matching task is to identify nodes in the two graphs that refer to the same concept. In this paper, we focus on the analysis of textual descriptions of the concepts. We employ neural language models as they can score well on text content similarity On the other hand, we show that the text similarity of entity descriptions does not equal to referring to the exact same entity. Our text-based multi-step system was among the top participants at the Knowledge Graph matching track of the Ontology Alignment Evaluation Initiative.
Transfer Learning Through Knowledge-Infused Representations with Contextual Experts

In recent years, transfer learning in natural language processing has been dominated by incredibly large models following a pretraining-finetuning approach. A problem with these models is that the increasing model size goes hand in hand with increasing training costs. In this work, we instead evaluate the transfer learning capabilities of the recently introduced knowledge-infused representations. Previously, these infused representations have been shown to infuse experts’ knowledge into a downstream model when the expert and downstream model operates on the same task domain. We extend this by investigating the effects of different expert task configurations on the performance of the downstream model. Our results show that differing expert and downstream tasks do not affect the downstream model. This indicates a desired robustness of the model towards adding irrelevant information. Simultaneously, the ability to transport important information is retained as we continue to see a significant performance improvement when adding two experts of differing tasks. Overall, this solidifies the potential knowledge-infused representations have regarding the ability to generalize across different tasks and their ability to recycle old computations for smaller new downstream models.
Automated Categorization of Mathematics Questions

Sorting out mathematic questions into respective topics manually is highly time-consuming but necessary. A reliable automated categorization algorithm to categorize mathematic questions is needed to speed up the process. In this paper, we present an accurate text categorization computer algorithm that categorizes junior college pure mathematics questions into corresponding chapters. This algorithm extracts keywords from the questions in the dataset and calculates the Jaccard similarity coefficient between questions based on the keywords extracted. Different LaTeX formulas were taken into consideration during keyword extraction. Upon close examination of the algorithm and by testing with sample questions, we concluded that the algorithm efficiently achieves high accuracy in classifying pure mathematics questions. Furthermore, a program with a graphic user interface was developed to provide a potential practical use of the algorithm.
Innovative Methods for Smart Education: Hybrid Approach

The development of intelligent and digital technologies explains the need for innovative learning tools in educational systems. They are a lot of modern digital tools for educational activities, which in many foreign works are described as smart technology (SmT), smart learning (SmL), smart environment (SmE) and smart pedagogy (SmP). These technologies and platforms are successfully applied in the new generation of smart universities (SmU), convincingly proving the effectiveness of modern e-learning. The aim of the study is to develop a model for training intellectual resources in higher education. The novelty of the proposed solutions includes the hybrid approach based on the integration of crowdsourcing management tools with ED-Tech and CLIL technologies for their implementation into education export programs and further evolution and maturation of smart education.
Evolving Connections in Metaverse

In this period of fast modernization advancement, the field of Internet and Web has a huge change in which people learn, communicate, connect, share information, etc. People still interact with the system using keyboard, mouse, and other monitoring systems. However, the primary modes of computer-based communication and connection remain unchanged. Though we have various new and emerging applications like social networking, video conferencing, Augmented Reality (AR) applications (e.g., Pokemon Go), and virtual 3D network models which provide a rise in digital transformation, we have a limited result because of conventional reasons and in need of insufficient network support or bandwidth. The term “metaverse” has been formulated to facilitate truly amazing experiences that go beyond our conventional lives. The metaverse can be thought of as a digital representation of the real world. Avatars are virtual representations of various human outlines, which is an approximation of the current real world. The concept of metaverse has now become a topic of debate in the field of digital transformation. As the metaverse grows, online spaces will emerge that provide more complex user interactions than current technology permits. Big tech giants are attempting to forge the path for the future. In addition to games and social media, metaverse will incorporate economic infrastructures, digital entities, and other applications. This paper gives a reasonable effort of an inexpensive approach that examines the new developments of the metaverse and the resulting digital transformation.
Adaptive Segmentation on Audio Watermarking using Signal Differential Concept and Multibit Spread Spectrum Technique

Audio data digital piracy is a crucial matter since it jeopardizes the original contents copyright. Spread spectrum (SS) audio watermarking is an effective method of preventing digital audio piracy. Numerous studies have been published about these spread spectrum (SS) audio watermarking technologies. However, those technologies did not have enough capacity and high robustness at the same time. Therefore, the audio watermarking system in this paper is based on a unique Adaptive Segmentation and the multibit SS-based that can embed multiple watermark bits into the host audio signal utilizing one random pseudo-noise (PN) to represent multiple watermark bits. By determining the highest response value of the second-order derivative of the original audio signal, the audio watermark in this paper is robust against typical assaults and has good performance against desynchronization attacks. The audio watermarking technique in this paper has high imperceptibility, has a large embedding capacity, good robustness, and has been proven to be effective through numerous simulations.
INTERTEXTUAL STUDY “WHEN YOU WERE MINE BY REBECCA SERLE AND ROMEO &amp; JULIET BY WILLIAM SHAKESPEARE”

This paper entitled Intertextual Study “When Were Mine by Rebecca Serle and Romeo &amp; Juliet by William Shakespeare”. The research method is a qualitative descriptive method with an intertextual approach. The problem discussed in this article is how the intertextual relationship between "When Were Mine by Rebecca Serle and Romeo &amp; Juliet by William Shakespeare". The scope of the problem discussed is the intrinsic elements and stories of novels and drama scripts whose intertextual relationships are reviewed from the theme and plot. Also describes the intrinsic elements and intertextual relationships “When Were Mine by Rebecca Serle and Romeo &amp; Juliet by William Shakespeare” in terms of themes and plot. Based on the results of the discussion, the intertextual relationship between “When Were Mine by Rebecca Serle and Romeo &amp; Juliet by William Shakespeare” is the similarity of theme and plot. In addition, there are differences between the two novels and the drama. The similarity between the two stories is that both novels have the theme of romance, containing teachings about human life to be able to behave and act according to logic.
Brief Announcement: Accelerate CNN Inference with Zoning Graph at Dynamic Granularity

Partitioning a CNN and parallel executing inference with multiple IoT devices have gained popularity as a way to meet real-time requirements without sacrificing model accuracy. However, existing algorithms have struggled to find the optimal model partitioning granularity for complex CNNs. Additionally, executing inference with heterogeneous IoT devices is NP-hard when the structure of the CNN is a directed acyclic graph (DAG) rather than a chain. In this paper, we introduce a versatile and cooperative inference framework that combines both model and data parallelism to accelerate CNN inference. DeepZoning employs two algorithms at different levels: (1) a low-level Adaptive Workload Partition algorithm that uses linear programming and takes spatial and channel dimensions into optimization during the search for feature map distribution on heterogeneous devices, and (2) a high-level Model Partition algorithm that finds the optimal model granularity and organizes complex CNNs into sequential zones to balance communication and computation during execution.
Ingliding as stylistic practice in Porto Alegre (RS)

This paper analyzes ingliding ([ ɛ ]~[ ɛ ɐ ], [ ɔ ]~[ ɔ ɐ ]) in the Brazilian Portuguese spoken in Porto Alegre (RS) in order to examine both the variation pattern as well as the styles and social meanings associated with the variants of the process. To achieve this goal, two samples were analyzed quantitatively (variable rule analysis of the speech data, Cf. Labov (2008 [1972])) and qualitatively (stylistic practices analysis, considering the speakers’ declared practices). In this work, style is treated as a practice of persona construction (ECKERT, 2005, 2012), a notion related to lifestyle , a set of tastes, beliefs and practices typical of a certain class or class fraction (BOURDIEU, 2015 [1979/1982]). The results show that ingliding is favored by open-mid vowels and disfavored by labial and dorsal following segments. The centring diphthong, which arises from the nuclear vowel itself in the prominent segments of the intonational phrase, seems to occur in elements of longer intrinsic duration. Inglided vowels are mainly produced by men and people in an intermediate age group, those who have a higher level of education and those in a higher socioeconomic stratum. These findings, interpreted in light of Bourdieu's social theory, suggest that ingliding can be a stylistic practice that provides symbolic profit to people in the post-modern paradigm of the youth movement that occurred in the Bom Fim neighborhood in the 1980s, who can index freedom , innovation , youth and transgression to the centring diphthong. There are currently at least two lifestyles of which ingliding is a part in the city of Porto Alegre. These styles, though in different ways, are associated with cool and laid-back meanings, such that the centring diphthong can construct personae opposed to a formal , uptight or snobby elite. The freedom associated with ingliding embodies a lifestyle that is marked by relaxation.
Densely stacked rebar counting based on image semantic segmentation in wide scene

Abstract To accurately count densely stacked rebars belongs to semantic segmentation in image. Various methods have been proposed to segment the objects in single image. However, widely and densely stacking scene such as warehouse or vehicle stacking of rebars is always faced. For these scenes, clear image of end faces of rebars cannot be obtained through a single camera. Generally, multiple cameras are often used to take clear images to count rebars. One strategy is to stitch multiple images into a single image at pixel level first, and then count rebars in this stitched image. Various methods have been proposed to stitch images, but it is extremely difficult to stitch two images at pixel level accurately for parallax distortion or partially hidden. A topological stitching strategy is proposed in this paper to count the rebars in whole scene. In the method, rebars in each image are identified and counted first, and then the identification and counting results in the adjacent images are stitched so as to esitmate the total number of rebars. The perfomance of the method is evaluated on real datasets, and the counting accuracy exceeds 99%. Experiments show that the proposed method is effective on widely and densely stacked rebars identification.
Meta-Learned User Preference Estimator with Attention Network for Cold-Start Recommendation

Abstract One crucial challenge in the recommendation research field is the cold-start problem. Meta-learning is a feasible algorithm to reduce the error of cold-start recommendation because it can adjust to new tasks rapidly through relatively few updates. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. We proposed a recommendation model called attentional meta-learned user preference estimator that combines attention network and meta-learning. This method enhances the ability of modelling the personalized user interest by learning the weights between users and items based on attention mechanism, then improves the performance of cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.
ANALISA SISTEM INFORMASI ALIH MEDIA REKAM MEDIS BERBASIS WEB DENGAN METODE PIECES DI RSUD DR. R. SOEDARSONO KOTA PASURUAN

Medical records are one part of the hospital that plays an important role in running the Hospital Information System (SIRS). The Medical Record Media Transfer System is a web-based application used to convert printed files into pdfs. This study aims to evaluate the application of medical record media transfer using the PIECES method. This research was evaluated with several PIECES factors, namely Performance, Information, Economy, Control and Security, Effeciency, and Service. The use of the PIECES method can discover how feasible a medical record media transfer application is to be used. The results of this study were in the form of testing from 10 samples using questionnaires and producing good category values. The medical record media transfer application can be declared suitable for use at Dr. R. Soedarsono Hospital, Pasuruan City.
Job Recommendation for Daily Paid All Workers

stract: In the last years, job recommender systems have become popular since they successfully reduce information overload by generating personalized job recommendation. Currently recommendation Systems are utilized to handle the issue of the overwhelming amount of data or the information in every domain and enables the clients to concentrate on data that is more relevant to their area of interest. One such field where recommender frameworks can play a vital role is to help workers who works on daily wages basis by recommending a job based on their skills and interest. In the current scenario, with an abundance of different industries and fields, a huge number of jobs are available for the skilled and literate professionals. It is not difficult to find suitable jobs for a person after his field has been identified but the main obstacle for achieving this goal is lack of information and awareness. The problem is that there is no such relevant recommendation system available currently so, we proposed the “Job recommendation system for daily paid workers” by analysing the skills of a particular worker and then finding appropriate jobs in his area of interest. To make this system even more robust, a wide variety of factors are taken into consideration while recommending jobs to a worker’s who work on daily wages basis
Student Achievements Portfolio Management using Blockchain

Certificates serve as a testament to an individual's credentials and proficiency in a certain field. They are official documents that attest to the acquisition of specific skills and knowledge. However, in a college setting, the credibility of certificates may be compromised due to factors such as counterfeiting, misuse, and improper storage. To address this challenge, a blockchain-based document verification system can be implemented. Blockchain technology is well-suited for this purpose due to its inherent immutability, which ensures the authenticity and integrity of the certificates. This system would consist of a feature rich user interface for certificate verification and issuance, combined with a blockchain-based storage mechanism. This would allow students to maintain a secure and verifiable portfolio of their certificates, thereby preventing any potential forgery or manipulation.
Multisource remote sensing image registration based on geometric constraints

With the continuous development of remote sensing technology, the types of remote sensing image data are more diversified. More spatial information of images can be obtained by multisource fusion. In addition, the complementarity between sensors can effectively overcome the limitations of a single sensor in complex environments. The registration of optical image and SAR image is the key point in multisource image registration. Optical images have high resolution. But are vulnerable to the impact of harsh environments, resulting in the loss of spectral details. SAR images have strong penetrability to vegetation, cloud, ice and snow. But they are interfered by speckle noise. The imaging mechanism of optical image and SAR image is different, and the difference of gray information is large, which may lead to the performance failure of two kinds of image registration. To solve the above problems, in this paper, we propose a method of optical image and SAR image registration based on geometric constraints, which optimizes the feature descriptor locally through the spatial geometric structure characteristics between similar feature points. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the matching accuracy.