Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF INFORMATION COMPETENCE OF PART-TIME TEACHERS OF PHYSICAL CULTURE OF YAKUTIA ON THE BASIS OF ADVANCED TRAINING COURSES

The article presents the need for the organization of advanced training courses in the direction of improving the level of information competence of physical education teachers in secondary schools of Yakutia. The purpose of the study is to substantiate the conduct of advanced training courses aimed at developing the information competence of teachers of physical education in schools of Yakutia by means of digital technologies. The research method is based on the use of the method of expert assessments and statistical processing of quantitative research results. The experiment involved more than 120 teachers of physical education by correspondence, who took advanced training courses on the basis of NEFU and CHGIFKIS from 12 uluses of the republic. As part of the implementation of planned advanced training courses, a characteristic feature of which is the availability of a material and technical base for providing the network and the Internet, a variety of platforms and territorial location. The overwhelming majority of schools in Yakutia are rural (more than 70%), since they function and work in rural settlements. The article updates scientific and methodological approaches to the study of the use of digital technologies and applications conducted for advanced training courses. The research conducted among physical education teachers justifiably testifies to the need to take into account the peculiarities of the educational environment of schools in Yakutia in the development of information competence of physical education teachers. The significance of the study lies in the development of the information competence of part-time physical education teachers using digital technologies and resources in the light of the new requirements of the Federal State Educational Standard.
Recommendations for implementing distributed ledger technology (blockchain) in the public sector of Ukraine based on global experience

Integrating new technologies such as distributed ledger technology into government systems is a multifaceted process characterized by numerous potential benefits, associated costs, and risks. Previous and existing pilot implementations of blockchain-based software solutions in the public sector have demonstrated that this technology can have varying impacts depending on contextual factors, including the specific type of chosen government service. Furthermore, within each distinct area of public services, the implementation of e-governance technologies can yield different outcomes for various stakeholders, including government entities, public servants, and citizens. Therefore, this article provides a review and analysis of global experiences with the utilization of distributed ledger technology in various domains of the public sector. To achieve this, several countries with advanced expertise in e-governance and the implementation of distributed ledger technology-based solutions were identified. Drawing from the experiences of these nations and considering the unique features, advantages, maturity level of blockchain technology, and existing solutions based on it, general recommendations were formulated regarding the implementation of distributed ledger technology (blockchain) in Ukraine’s public sector.
Impacts of Blockchain on Accounting in the Business

There have been many studies on the blockchain, however blockchain research in the accounting field is scarce. The article outlines the characteristics and steps to apply blockchain in accounting. The article also identifies and measures the factors affecting the application of blockchain in accounting in enterprises, proving that the application of blockchain impacts the quality of the accounting information system of enterprises. The article shows that incorporating blockchain in accounting will help the accounting at businesses optimize the security, safety, and transparency of accounting information. We collected data on 195 manufacturing firms in six sectors. This study’s primary data analysis method is the SEM structural equation modeling method. The article used AMOS software to evaluate and measure the influence of each factor on the application of blockchain and the effect of blockchain on the accounting information system in enterprises. The article analyzed four aspects: the level of information technology of the accountant (IT), Information Security Infrastructure (SI), Training (TR), and Legality and Regulation (LR), affecting the application of blockchain in the business. The independent variables TR (training) and IT (information technology) have an impact on BL (Blockchain). We also find that the independent variable IT (information technology) has the most substantial impact on Blockchain adoption, followed by the independent variable TR (training), which has the second most influential impact on Blockchain adoption. For the accounting information system, the results show that applying blockchain will substantially impact accounting information, along with the safety factor of information infrastructure SI also has an evident influence. The results show that applying blockchain in accounting, information technology, and professional training are core issues with significant influence. The issue of guaranteed infrastructure also determines the effectiveness and efficiency of blockchain applications for accounting information systems. I declare that this is my scientific work; it is my original work. I commit that all data sources and ideas are my research and have never been published anywhere.
Structure-Preserving Image Smoothing using Adaptive Bilateral Filter

Structure-preserving image smoothing is an important image processing problem that plays significant role in many applications of image processing and computer vision such as detail enhancement, edge detection, tone mapping, image segmentation, and image abstraction. We suggest a structure-aware bilateral filter to accomplish smoothing on an image without altering the salient structure information. The main contribution of the proposed work is the designing of the scale map, which has been used to choose the size of the spatial kernel at each pixel in accordance with the structure information. The aim behind using the scale map is to perform filtering on the homogeneous and texture regions while preventing filtering on the prominent structure regions. The proposed method has excellent structure-preserving and texture removal properties. The qualitative and quantitative analysis of the experimental results has shown the outperformance of the proposed method over the existing state-of-the-art methods.
YOLO-BAM: Integrating CBAM to the YOLOv3 Model for Pedestrian Detection in Images

This study investigates the impact of integrating the Convolutional Block Attention Module (CBAM) into the YOLOv3 model for pedestrian detection. Through a 50-epoch training process on the COCO 2017 dataset, the performance of the modified YOLOv3 model, named YOLO-BAM, was evaluated against the baseline model. The results revealed that YOLO-BAM demonstrated a modest increase in accuracy with a 2.6% improvement compared to the baseline model. YOLO-BAM achieved a mean Average Precision (mAP) of 55.020%, while the baseline model attained an mAP of 56.011%. These findings suggest that factors such as the dataset, the CBAM implementation, the inherent effectiveness of the YOLOv3 model, and the evaluation metrics employed may have contributed in not observing more significant improvements in the modified model. Further analysis and exploration are necessary to uncover the full potential of integrating CBAM into YOLOv3 for pedestrian detection.
Requirements Review Education Curriculum for Embedded Software Practitioners

The author teaches requirements engineering for embedded software engineers in Japan. Conventional requirements review methods based on overall quality characteristics such as clarity and feasibility have the problem that the interpretation of quality characteristics becomes dependent on the individual. In our education program, we teach the requirements review method based on the terminology of the specification sentences. In this paper, we introduce the content of the teaching and the resulting impressions of the requirements review method.
The Utilization of Educational Apps in Teaching English Vocabulary for Elementary Students at a Vocational School

The English teachers are always seeking for new methods to improve their English classes. If teachers wish to boost language acquisition, they must make them more engaging for students. Today, if teachers want to be successful in their proffession, they must include technology into their English classes. If it is vocabulary lessons, there are English language apps where students may compete against their friends to see who can get the highest score or match vocabulary items with words. Students will find this motivating and engaging. This study employed the Delphi method to identify which educational apps are really suitable for the teaching and learning vocabulary at a vocational school for elementary students. 21 English teachers were invited to participate in a two-round Delphi survey. The survey revealed 7 apps that are considered suitable and effective for students at this school. The findings are important for the English department at the studied context in developing the appropriate teaching strategies. It is also a reference for English teachers in general when deciding apps for their teaching.
LinkDoc: An Automated Process in the Delivery of Documentation in a Global Software Development Environment

Documentation, among the phases of software development, is a crucial step for product acceptance, given that poorly made documentation can compromise product quality. In the context of mobile development, it is not always easy to deal with the rise in the number of released devices, the new technologies and specifications, the frequent updates to the Android operating system and the lack of experience of the people responsible for the development itself. Considering this, the authors of this paper conducted an experiment aiming to assist the development of technical documentation, reducing the time spent preparing and raising the degree of reliability in this information. Through simulations in a controlled environment, a group of employees were able to utilize this new approach to prove its efficiency. The automation of this process shows a reduction of up to 33.2%, that is, 10.3 hours less in the documentation’s total execution time. Furthermore, this paper shows that it is possible to raise the reliability of information, since the average error rate shown is around 5.3%. However, for employees who have an average experience level in the team (between 6 and 18 months), the error rate is under 1.7%. In a setting where one must deal with adverse variables, such as new employees, off-standard databases, a rise in demand, among others, those values are substantially satisfactory and the new process becomes essential for the team.
Boosting Deepfake Detection Features with Attention Units

One of the emerging problems of deep learning technology is deepfake videos with easy access to powerful and inexpensive computing power; The internet is littered with fake material like fake photos, videos, and audios. People’s identities, privacy and reputations are at risk due to the widespread proliferation of fake media content. Since videos can have a potentially destructive effect on society, establishing their legitimacy is crucial. Thus, we investigate different attention mechanisms in this paper for deepfake detection. In videos, attention mechanisms are responsible for directing the convolutional Neural Network’s (CNNs) emphasis to the most critical parts of the frame in terms of both content and context. Therefore, we answer the question: How do you apply attention to deepfake detection? And what form of attention is effective for deepfake detection tasks? To address these concerns, we conduct research and experimental testing on videos that have been manipulated using four different methods drawn from the FaceForensics++ dataset. We conduct a cross-data evaluat ion for the network with and without attention to assess the network’s capacity to detect previously unseen manipulated images. The proposed approach outperformed conventional Convolutional Neural Networks for deepfake detection by 8% AUC performance.
Object Detection Meets LLMs: Model Fusion for Safety and Security

This paper proposes a novel model fusion approach to enhance predictive capabilities of vision and language models by strategically integrating object detection and large language models. We have named this multimodal integration approach as VOLTRON (Vision Object Linguistic Translation for Responsive Observation and Narration). VOLTRON is aimed at improving responses for self-driving vehicles in detecting small objects crossing roads and identifying merged or narrower lanes. The models are fused using a single layer to provide LLaMA2 (Large Language Model Meta AI) with object detection probabilities from YoloV8-n (You Only Look Once) translated into sentences. Experiments using specialized datasets showed accuracy improvements up to 88.16%. We provide a comprehensive exploration of the theoretical aspects that inform our model fusion approach, detailing the fundamental principles upon which it is built. Moreover, we elucidate the intricacies of the methodologies employed for merging these two disparate models, shedding light on the techniques and strategies used.
Report on the Status Quo and Development of Short-Form Video Research in China

The study used the keyword “short-form video” to search the China National Knowledge Infrastructure (CNKI). The search period ran from the database’s inception to December 31, 2022, with the extraction date set for March 3, 2023. A total of 3,938 articles were found. The study employed bibliometrics, as well as co-occurrence analysis software BICOMB 2.04 and visualisation analysis software Citespace, to examine the status quo and development characteristics of the short-form video domain in China from the perspectives of publication quantities, research institutions, authors, literature sources, research hot topics, and so on, to identify future research directions. The purpose of this study is to provide new research perspectives for related researchers as well as scientific references for better development of China’s short-form video industry.
Consultation Paper on Artificial Intelligence and Civil Liability

Artificial intelligence is pervasive in today’s world. Decisions, predictions and recommendations made by artificial intelligence affect individual lives to an ever increasing extent.The benefits of artificial intelligence are great, but they also come with risks. When the risks materialize, harm to persons and property may result. Avoidable harm to persons and property brings the law of tort into play.Tort is the branch of law concerned with non-contractual civil wrongs. Its principles evolved in order to deal with harmful human conduct. Involvement of artificial intelligence as a causal factor leading to harm complicates the application of those principles to determine who is legally responsible, when and for what. Many questions concerning rights and liabilities in that context have yet to be answered because these situations are new, and the law relating to artificial intelligence is unsettled.This consultation paper is issued under the Artificial Intelligence and Civil Liability Project, which BCLI initiated in late 2021. The objectives of the project are:• to identify how the common law rules of tort need to be adapted to provide just and adequate civil remedies for harm to persons, property, and other legal interests resulting from the operation of autonomously functioning artificial intelligence; and• to develop and publish law reform recommendations addressing that context.BCLI is assisted in carrying out the project by the Artificial Intelligence and Civil Liability Project Committee, an interdisciplinary group reflecting expertise in computer science, engineering, and medicine as well as law.
Female Unemployment and the Procedure That a Woman Has to Go Through to Start a Business: Microfinance Policy Thresholds

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Non-fungible token valuation exposed: US Generally Accepted Accounting Principles Compliance and artificial intelligence’s disruptive role

The ascent of non-fungible tokens (NFTs) has recently garnered significant attention, prompting the need for effective methods to appraise these digital assets. An examination was conducted to ascertain the efficacy of the US Generally Accepted Accounting Principles (GAAP) in valuing NFTs, considering their unique attributes, notably their indivisibility and blockchain-based ownership. With a two-fold aim, this study conducts a comprehensive evaluation of GAAP’s suitability for NFT valuation while pinpointing the constraints inherent in the existing accounting framework. The proposed remedies encompass the development of industry-specific guidelines (ISG), refinement of NFT categorization and evaluation techniques, timely resolution of valuation complexities, and integration of artificial intelligence (AI) based solutions. Preliminary findings reveal that conventional GAAP procedures offer only partial alignment with the intricacies of NFT valuation, primarily because of the unprecedented nature of NFTs and swiftly evolving market dynamics. This investigation contributes significantly by delving into the subtleties of implementing established accounting principles within this nascent digital asset class. Furthermore, it underscores the importance of interdisciplinary collaboration between the accounting and technology sectors to adeptly navigate the ever-transforming domain of blockchain technology and digital assets. This research provides a resource for professionals, regulators, and scholars engaged in the dynamic realm of NFT valuation and accounting.
Video expert assessment of high quality video for Video Assistant Referee (VAR): A comparative study

Abstract The International Football Association Board decided to introduce Video Assistant Referee (VAR) in 2018. This led to the need to develop methods for quality control of the VAR-systems. This article focuses on the important aspect to evaluate the video quality. Video Quality assessment has matured in the sense that there are standardized, commercial products and established open-source solutions to measure it with objective methods. Previous research has primarily focused on the end-user quality assessment. How to assess the video in the contribution phase of the chain is less studied. The novelties of this study are two-fold: 1) The user study is specifically targeting video experts i.e., to assess the perceived quality of video professionals working with video production. 2) Six video quality models have been independently benchmarked against the user data and evaluated to show which of the models could provide the best predictions of perceived quality. The independent evaluation is important to get unbiased results as shown by the Video Quality Experts Group. An experiment was performed involving 25 video experts in which they rated the perceived quality. The video formats tested were High-Definition TV both progressive and interlaced as well as a quarters size format that was scaled down half the size in both width and height. The videos were encoded with both H.264 and Motion JPEG for the full size but only H.264 for the quarter size. Bitrates ranged from 80 Mbit/s down to 10 Mbit/s. We could see that for H.264 that the quality was overall very good but dropped somewhat for 10 Mbit/s. For Motion JPEG the quality dropped over the whole range. For the interlaced format the degradation that was based on a simple deinterlacing method did receive overall low ratings. For the quarter size three different scaling algorithms were evaluated. Lanczos performed the best and Bilinear the worst. The performance of six different video quality models were evaluated for 1080p and 1080i. The Video Quality Metric for Variable Frame Delay had the best performance for both formats, followed by Video Multimethod Assessment Fusion method and the Video Quality Metric General model.
Motivation and Parental Supervision Improve Indonesian Language Learning Outcomes

The learning process carried out by students is still low. This is because students are less motivated in learning. The purpose of this study is to analyze parental motivation and supervision on Indonesian language learning outcomes. This type of research is quantitative research. The subjects in this study were 30 grade IV students. The methods used in collecting data are interviews, questionnaires, observation, and documentation. The instrument used in collecting data is a questionnaire. The technique used to analyze the data is descriptive qualitative-quantitative. The results of the t test show that there is a partial positive influence of parental supervision variables on learning outcome variables. Based on the results of the large test of influence together on learning motivation and parental supervision on learning outcomes. The results of the multiple regression test showed that there was a significant influence on learning motivation and parental control variables on the Indonesian language learning outcomes. It was concluded that learning motivation and parental supervision can improve learning outcomes in Indonesian for fourth grade elementary school students. The implication of this research is that increasing motivation and parental supervision of children has an impact on increasing children's learning outcomes, especially in Indonesian language lessons.
VAK-Based E-Module for Grade IV Elementary School Students

Lack of teacher ability to develop teaching materials causes boredom. The aim of this research is to develop a Visual Auditory Kinesthetic (VAK) based learning e-module aimed at students in the fourth grade of elementary school, a solution that researchers can offer. This research is a mixed method-based development research using the 4 D (Four-D) research model. Data collection methods use observation, interviews and questionnaires. The research subjects were 4 experts and 1 teacher. The test subjects were shown to fourth grade elementary school students. The data analysis technique uses qualitative and quantitative descriptive analysis. The research results show that material experts and media experts carry out product testing, while teachers and students carry out assessments. The results show overall results for material experts are 91 percent in the very good category, overall media experts are 89 percent in the very good category, practitioners overall are 95 percent in the very good category, and students overall are 93 percent in the very good category. Therefore, it can be concluded that learning e-modules that focus on energy-saving materials for fourth grade elementary school students are suitable for use. The implication of this research is that this learning e-module can help teachers and students as a learning tool to support learning, especially in energy saving material.