Reviewerly/exHarmony huggingface dataset:
Analysis of Pattern Recognition of Disorders in Children Using Artificial Neural Network (ANN) and Decision Tree Methods: A Case Study of Disorders in Children with Disabilities

Abstract In this era of globalization, technological developments in the field of digital information have greatly affected services in the health sector in healing and meeting public satisfaction. Digital technology-based services are needed by children with disabilities so that they can be given superior and effective treatment. Digital technology services are used as a clustering and pattern recognition system using Artificial Neural Network (ANN) and decision tree. The data collection process is done by taking a video of a child walking on a track in the form of manila cardboard whose feet have been colored with food coloring so that the footprints can be seen clearly. After that, Artificial Neural Network (ANN) and decision tree have two stages, namely the training stage and the testing stage. The training stage is carried out using 20 respondent data from children who walk. The testing stage is carried out using three new data that are different from the training stage. From these two methods, a high accuracy rate of 99.91% is obtained for ANN and close to 100% for decision trees.
A development of a calibration method for field pyranometers using satellite image data

Abstract In this work, a calibration method for deriving a sensitivity of field pyranometers using Himawari satellite image data was developed. The purposed method representing a relation between 10–bit digital counts ranging between 0–1023 from satellite data and global solar irradiance of a standard pyranometer measured at Nakhon Pathom (13.82 °N, 100.04 °E) was investigated and it was found to be an exponential relation. Afterward, this model was applied to estimate the sensitivity of field pyranometers installed at three meteorological monitoring stations located in the main regions of Thailand namely, Chaing Mai in the North (18.78 °N, 98.98 °E), Ubon Ratchathani in the Northeast (15.52 °N, 104.87 °E) and Songkhla in the South (7.20 °N, 100.60 °E) for the validation. The results showed that the sensitivity of field pyranometers derived from satellite data and that obtained from the standard outdoor calibration method of ISO9847 was in a very good agreement with a discrepancy between both datasets of about 1%.
Investigating the linear equations of maximum and minimum slope lines on a linear graph in high school physics experiments using numerical method

Abstract This study investigates the maximum and minimum slope lines of a linear graph used in high school physics experiments. The uncertainty of the graph is calculated by considering the steepest and shallowest possible lines. However, manually drawing these lines can be challenging. Thus, this research employs numerical calculation method to determine the slopes of the two straight lines that pass through the number of error bar of y-data as much as possible. These lines are drawn between the lower edge of the error bars and the upper edge of the error bars of two data points on the graph. Two case studies are conducted using physics’ experimental data, which includes three columns; x-data, averaged y-data and standard deviation of the sampling distribution of means. The results are presented as data points with error bars on graph, along with the maximum and the minimum slope lines and their corresponding linear equations. Further analysis of uncertainties of slope and Y-intercept can be performed using these equations. These findings are valuable for researchers, educators, and students to interpret and report the results. In addition, a web application is presented to facilitate the determination, further enhancing the study’s accessibility and usefulness.
Blockchain-Empowered Decentralized Philanthropic Charity for Social Good

The charity sector impacts society significantly in many areas, including providing education, healthcare, hunger relief, drinking water, disaster relief, environmental preservation, and assistance to underserved people. The existing charity organizations have numerous limitations, such as poor management, high operation costs, and a lack of transparency in the donation execution flow. The authentication of users and institutions is a big problem in the existing system. This research resolves the issues of transparency and reliability with an immutable and traceable distributed ledger. We empower the existing centralized charity works with the electronic know-your-customer (eKYC) authentication approach and cryptographic HASH. Information privacy is implemented using the filters within smart contracts. The implementation of eKYC to ensure authenticity and to secure data flow through the channel are two significant contributions of this work. A coin-toss function for data selection and a random time delay between pieces of data are used to avoid attacks based on guesswork. We aim for this framework to send 100% of donations to the beneficiaries and become a hyper-liquid medium to fill the United Nations Sustainable Development Goals (SDG) funding gap. We also introduce the concept of service charity to broaden the ability for people to offer their services and skills as charity.
Design and development of OCR software for remote measurement and calibration

Abstract The advent of digital technology has led to significant changes in work processes in many organizations and industries worldwide including Thailand. This work aims to apply digital technology to improve measurement and metrology in terms of accuracy, productivity, and variability. We developed machine vision software for monitoring and collecting digital data from measurement systems. The application of Optical Character Recognition (OCR) technology in metrology is the first step toward digitalization, enabling enterprises to improve their measurement processes by automating data collection, analysis, and management. Moreover, it can be integrated with machine learning algorithms to enhance the automation of complex measurement tasks. To achieve this goal, an OCR software for data recording was designed in the LabVIEW environment. The image captured by the camera is processed by the LabVIEW OCR system which is trained continuously until it can recognize characters consistently and accurately. This software has been tested with the calibration systems which has a seven-segment digital format. The result shows that the software can acquire and transmit the instrument reading with reliable and exceptional accuracy. In addition, it can also reduce the steps and time required for measurement. In conclusion, the application of OCR technology can improve the efficiency, productivity, and accuracy of measurement. It also has a promising potential to enable remote measurement and calibration according to ISO/IEC 17025 possible.
The Genetic Algorithm for Creating Travel Routes in Religious Tourism Destinations in Bogor Regency, West Java Province

Abstract Religious tourism is an activity that is commonly carried out in Indonesia. One of the activities of religious tourism is the community’s habit of visiting sacred graves. Bogor, a city with a long history of 540 years, has many of these sites that attract tourists. This religious tourism activity brings economic, social, and environmental potential and consequences. The distance between the Sacred Tombs scattered at various points requires efficient and effective destination route planning. The application of the optimization method with the Particle Swarm Optimization (PSO) Genetic Algorithm can be a solution for designing an effective travel route. It can be beneficial for improving the economy, in this case, increasing the sales turnover of Bogor small and medium enterprise businesses. The conclusion drawn from the research is that applying a genetic algorithm will create a more efficient route set and will have an impact on saving fuel costs, reducing emissions and providing benefits to the development of the economic sector.
Digital Local History and Lore Exploration: Strategies and Tools of Educator’s Vocational Competency Development

The research topic is predetermined by overwhelming transformations of modern educational activities and vocational competency of school teachers as educative tour guides under present conditions of Digital local history and arts Web 2.0–3.0 representation and exploration development, as well as educative tour initiative booming. The objective of the research is to substantiate theory and propose a model of up-to-day productive system of strategies and tools for vocational competency and competitiveness of school teachers as educative tour guides. The research hypothesis purports that (a) vocational competency and competitiveness formation of teachers as educative tour guides needs to be directed at multimodal organization, tuning and support of actual educative tour initiatives; (b) the vocational educative tour skills competitiveness of teachers is based on integrate power of vocational hard and soft skills, flexible task-solving strategies, digital literacy and digital educative products making and multimodal communications skills acquisition and development. Within complex and ecosystem approach a set of expedient methodological strategies, tools and solutions has been proposed, elaborated and tested. The method is based on a cluster approach to competency formation, the multilevel model of vocational educative competency acquisition, a vertical model of increasing the level of competency, associated with an increase in the level of qualifications through the consistent development of digital age competencies of different levels of complexity; and finally—a functional-axiological approach, that reshapes the system of vocational competences based on the principle of tasks priority. As a result, the relevant to boosting vocational competency of school teachers as educative tour guides description of educational roadmaps and a systematic approach to different genres of digital educative products has been provided and tested. The set of productive digital educative genres includes: (a) local history and lore information project; (b) interactive digital guide; (c) virtual local history and lore excursion tour with exploration elements; (d) digital interactive local history and lore quest.
Digital Competencies of Student Labor Teams’ Members

The early involvement of young people in professional activities by means of student labor teams opens up opportunities for the formation of competencies that allow them to actively integrate into the world of the digital economy, master modern technologies and be ready to apply them in their professional activities. According to the authors, in the modern scientific community and among practitioners of education, insufficient attention is paid to the opportunities provided by training and work in student teams to form the specialists necessary for the modern economy. The chapter analyzes the possibilities of student labor teams in the formation of digital competencies; a number of features in the requirements of employers regarding digital literacy have been identified. Using the method of theoretical analysis and systematization of scientific sources, a generalization of ideas about the professionally important and personally important qualities of future professionals was carried out, the components of digital competencies in the structure of competencies of fighters of student labor teams were identified, and differences in ideas about the need and possibilities for the formation of various qualities and competencies, including digital ones, in the conditions of SLT were identified. A comparative analysis of the results of surveys of employers and members of student labor teams shows the actual demand for digital skills, both on the part of employers and members of student labor teams, and, according to a number of indicators, the importance of employers is higher than that of future SLT “fighters”. This is due to a deeper understanding by employers of labor functions and the specifics of the activity. The results show the presence of a problem field in the field of preparation for work in labor teams in general and in the formation of digital literacy competencies among their members.
Viscous Behavior of Fluids in the Eyes of Adults: A Global Survey

The viscous behavior of fluids can be observed in numerous everyday situations. Therefore, it is reasonable to assume that people, since they are usually not specialists in fluids’ flow behavior, possess naïve conceptions about the viscous behavior of fluids. These conceptions more or less deviate from corresponding scientific explanations. Qualitative studies with preschool children and secondary school students from Germany have already identified various naïve conceptions about the viscous behavior of fluids (e.g. that the density or stickiness of a substance explains its viscous behavior). Within the present study, we explore the question of whether similar naïve conceptions can also be found among adults around the globe. To this end, based on previous research, an online questionnaire was developed to survey adults worldwide regarding their naïve conceptions about the viscous behavior of fluids. The survey was conducted anonymously, online, and voluntarily in spring 2023; participants were recruited via SurveySwap. A total of 406 adults from all regions of the world (primarily Europe and North America) participated in the survey. In this paper, we report and discuss the main findings of this online survey.
An Online Course as the Basis for Training of Modern Volunteers

The chapter offers a new solution for the training of volunteers—the online course “Fundamentals of volunteering”. This course was developed by the authors and has been implemented on the basis of Immanuel Kant Baltic Federal University (Russia, Kaliningrad) since 2020, has been implemented on the lms.kantiana platform since 2021. The course is available to students of all forms and levels of education. This course is designed for those who are interested in volunteering and intend to start a volunteer practice. The course program is aimed at the formation of competencies in accordance with the labor functions of a teacher, facilitator, educator, or social worker. The purpose of the course “Fundamentals of volunteering” is to form professional competence in the field of organizing work with youth by mastering the basic knowledge of the specifics of volunteering by students, taking into account the experience of implementing practices in the field of volunteering. The program of the online course is described, examples of tasks for future volunteers and an assessment system are given. The significance and timeliness of the implementation of this online course is discussed: Hosting the 2018 FIFA World Cup in Russia, The COVID-19 pandemic, significant government measures to support volunteers, and etc.
Joint Item Recommendation and Trust Prediction with Graph Neural Networks

Item recommendation and trust prediction are desired by users on social network platforms since they can help users find their favourite items or friends faster. Existing methods usually utilize users’ social relationships to facilitate the item recommendation task and leverage users’ preferences to assist the trust prediction task. While sociological researchers have shown that users’ preferences and users’ social relationships are not isolated but influenced by each other, there are a few studies that model these two tasks jointly. However, in these studies, the incorporation of distrust relations and the dynamic item-specific mutual influence between users’ preferences and users’ social relationships are ignored. In this paper, we propose a joint learning method using graph neural networks for item recommendation and trust prediction tasks, named JoRTGNN. First, users’ distrust relations are incorporated along with trust relations to extract more potential user social relationships, which can benefit both the item recommendation and trust prediction tasks. In addition, a dynamic item-specific mutual influence between users’ preferences and users’ social relationships is highlighted with attention mechanisms for the joint learning tasks. Experiments have been conducted on the Epinions datasets, and the experimental results show a superior performance of JoRTGNN over baselines in item recommendation and trust prediction tasks, which demonstrates that item recommendation and trust prediction can be effectively improved in the process of the mutual influence between users’ preferences and users’ social relationships.
MULTICULTURALISM IN CYBERSPACE

Language is an essential part of identity, and in many ways it is considered to be the fundamental constituent of identity. Language and culture are inextricably linked. Every social group with distinct linguistic and social characteristics seeks to preserve its cultural identity. Contemporary political theorists have labeled this phenomenon of the coexistence of different cultures in the same geographical space as multiculturalism. Based on the descriptive, analytical, comparative and verbal-culturological methods, this article discusses the presence of various cultural and ethnic groups within a society. Multiculturalism in cyberspace mainly denotes the harmony and interaction of diverse cultures within online communities and platforms. The vanishing of cultures naturally has a negative impact on diversity. It is critical to create an environment where different cultural groups can sustain and flourish, thus making the overall gamma of linguistic colors richer and more colorful.
Societal Implications of Recommendation Systems: A Technical Perspective

Abstract One of the most popular applications of artificial intelligence algorithms is in recommendation systems (RS). These take advantage of large amounts of user data to learn from the past to help us identify patterns, segment user profiles, predict users’ behaviors and preferences. The algorithmic architecture of RS has been so successful that it has been co-opted in many contexts, from human resources teams, trying to select top candidates, to medical researchers, wanting to identify drug targets. Although the increasing use of AI can provide great benefits, it represents a shift in our interaction with data and machines that also entails fundamental social threats. These can derive from technological or implementation mistakes but also from profound changes in decision-making. Here, we overview some of those risks including ethical and privacy challenges from a technical perspective. We discuss two particularly relevant cases: (1) RS that fail to work as intended and its possible unwanted consequences; (2) RS that work but at the possible expense of threats to individuals and even to democratic societies. Finally, we propose a way forward through a simple checklist that can be used to improve the transparency and accountability of AI algorithms.
The Relevance of Deepfakes in the Administration of Criminal Justice

Abstract Nowadays, it is challenging to distinguish between genuine content created by humans or deepfake created by deepfakes algorithms. Therefore, it is in the interests of society and nations to have systems that can notice and evaluate the content without human intervention. This paper presents the challenges of artificial intelligence, specifically machine learning and deep learning, in the fight against deepfake. In addition, it presents the relevance that deepfakes may have in the administration of criminal justice.
Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis

Accurate human posture classification in images and videos is crucial for automated applications across various fields, including work safety, physical rehabilitation, sports training, or daily assisted living. Recently, multimodal learning methods, such as Contrastive Language-Image Pretraining (CLIP), have advanced significantly in jointly understanding images and text. This study aims to assess the effectiveness of CLIP in classifying human postures, focusing on its application in yoga. Despite the initial limitations of the zero-shot approach, applying transfer learning on 15,301 images (real and synthetic) with 82 classes has shown promising results. The article describes the full procedure for fine-tuning, including the choice for image description syntax, models and hyperparameters adjustment. The fine-tuned CLIP model, tested on 3826 images, achieves an accuracy of over 85%, surpassing the current state-of-the-art of previous works on the same dataset by approximately 6%, its training time being 3.5 times lower than what is needed to fine-tune a YOLOv8-based model. For more application-oriented scenarios, with smaller datasets of six postures each, containing 1301 and 401 training images, the fine-tuned models attain an accuracy of 98.8% and 99.1%, respectively. Furthermore, our experiments indicate that training with as few as 20 images per pose can yield around 90% accuracy in a six-class dataset. This study demonstrates that this multimodal technique can be effectively used for yoga pose classification, and possibly for human posture classification, in general. Additionally, CLIP inference time (around 7 ms) supports that the model can be integrated into automated systems for posture evaluation, e.g., for developing a real-time personal yoga assistant for performance assessment.
The consideration of semantic gap between design and code

In project management on creation of program application, specialists from different subject areas are involved, who include their contributions, for instance, UI/UX designers who create mock-ups of the future application or developers who write the code according to the prototype. The design conception may go beyond the possibilities of interpreting it from a technical point of view of implementation. The realization of such idea could not to be able to collect on only one defined program platform or language, and accordingly the problem is appeared. To eliminate semantic gap between the designers’ concepts and opportunity of program developers in technical affordance, released methodology, Model Driven Architecture (MDA), which is, on the one hand, a concept for implementation of software, on the other hand a standard. In paper, considerate the MDA and its transformation levels with determine a pragmatical semantics of mapping, reasons of chosen a class diagram as model of transformation and Java language for code generation.
Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

As the key route detection device, the performance of marine LiDAR in harsh environments is of great importance. In this paper, a metric reliability analysis method for marine LiDAR systems under extreme wind loads is proposed. First, a static measurement accuracy evaluation model for the LiDAR system is proposed, targeting the problem that the LiDAR measurement tail reduces the measurement accuracy. Second, the distribution of extreme wind speeds in the Pacific Northwest is investigated, and a wind load probability model is developed. Finally, the impact of hull fluctuations on LiDAR measurement accuracy is analyzed by performing hull fluctuation simulations based on the wind load probability model, and the relationship curve between the metric reliability and measurement accuracy of marine LiDAR systems under extreme wind loads is addressed using the Monte-Carlo method. Experimental results show that the proposed LiDAR static measurement accuracy evaluation model can improve the measurement accuracy by more than 30%. Meanwhile, the solved curve of the LiDAR metric reliability versus the measurement allowable error indicates that the metric reliability can reach above 0.89 when the allowable error is 60 mm, which is instructive for the reliable measurement of marine LiDAR systems during ship navigation.