Reviewerly/exHarmony huggingface dataset:
System for the Synchronisation of Position, Motion and Audio Between MR and VR

The desire to share experiences and spaces with others is a behaviour based on human social and validation needs, and as digital technology develops, richer information tends to be shared Experiences in MR and VR are subjective experiences using head-mounted displays, but the technology to share these with others is still developing In particular, in MR the user is able to share information while looking at a real space. In particular, MR is an experience where additional digital information can be viewed while looking at a real space, but if this is to be communicated to others in remote locations, the space and the additional digital information need to be shared simultaneously. Therefore, in this research, a system has been constructed to allow remote VR users to experience the experience of MR users, with the aim of sharing the MR experience with a sense of realism. In this system, real-time synchronisation between MR and VR users is achieved, and the uploading and placing of 3D objects is realised, so that the experience of MR users is shared in real-time with VR users in remote locations. The system is designed so that the position, movement and sound of users in both MR and VR are synchronised and shared. The position, movement and voice of both MR and VR users are synchronised and shared. User tests have shown that MR-VR user collaboration makes users feel as if they are together without spatial separation, and that the webspace content editor is valuable when users want to make sense of and share the space. This paper presents the collaboration system and the web space content editor.
Automated Image Generation Reflecting Current Status of PoIs for Supporting On-Site Tourist Destination Selection

To support the decision-making process for selecting upcoming tourist spots (PoI: Points of Interest), it is necessary to share the tourist destinations' current status. Methods for sharing the current status of PoIs could include installing live cameras or having tourists share real-time photos. Still, there are challenges in terms of installation and maintenance costs and privacy concerns. In this paper, a tourism support system is proposed to aid in the decision-making of which PoI to visit next by automatically generating PoI current status images from template images and contextual information of tourist destinations while preserving privacy and presenting them to users. In the proposed system, semantic segmentation is used to divide template images into categories such as sky class, trees/grasses class, crowd class, and overall class. Based on the separately collected contextual information (weather, condition of trees/grass, crowd density, time, etc.), appropriate image transformations (using style transfer, etc) are applied to each category. The proposed system consists of an automated method for generating PoI status images and an application that presents these images to users. As part of a tourism experiment using the proposed system, 11 individuals aged 20 to 30 participated in a roughly 2-hour tour of Nara City in Japan. The results showed that compared to presenting template images, the proposed method yielded a decision-making score improvement of 0.27 and a similarity (to the current status) score improvement of 0.45 (out of five), both of which were statistically significant.
EmoGO: Emotion Estimation on Gazed Object by Using Mobile Eye-Tracker

Urban environments consist of diverse elements influencing residents' quality of life and emotional well-being. Prior research has attempted to map citizens' emotions using sensors and GPS data, but it remains difficult to pinpoint environmental triggers that evoke specific emotions of them. To solve this problem, we introduce EmoGO, a novel system that integrates mobile eye-tracking with emotion, visual, and GPS data collection. To our knowledge, this approach is the first study to uniquely correlate specific visual stimuli with emotions. Given the rising prevalence of eye-tracking in VR, MR, and AR technologies, EmoGO's potential applications are substantial. Our preliminary evaluation resulted in a promising result in emotion estimation on our original dataset, and also showed that the system can effectively collect real-world data on objects and associated emotions.
Information Fusion Analysis for Evaluating Educational Equity

The present study aims to analyze the situation of educational inequality in Ecuadorian rural students during the study period, by identifying the barriers and factors that affect their access to equitable and quality education. The results showed a marked digital educational gap between rural and urban students, aggravated by the lack of access to technological resources and connectivity in rural areas. The transition to virtual education during the pandemic exacerbated these inequalities, making it difficult for rural students to learn and limiting their integration into the education system. In conclusion, the need to implement comprehensive public policies to address these inequalities and promote educational equity is highlighted, considering the evaluations and weightings carried out through the MULTIMOORA method. For this, an investment in educational infrastructure, the provision of devices, and teacher training in educational technologies are required to improve access to quality education in rural areas. With this, the path towards a fairer and more inclusive future for all Ecuadorians is sought.
Classification of Datasets Based on Combination Algorithm of Clustering and Neural Network

How to get the needed information from the data accurately and effectively for analysis is a hot research topic nowadays. Accurate classification of data is the basis for smooth data analysis. In order to classify data effectively, scholars have proposed some classification algorithms, and the most frequently mentioned one is k-means algorithm. However, in previous studies, scholars have directly determined the number of classes to be classified for the data set to be classified. Therefore, in this paper, a combinatorial algorithm is proposed to improve the classification of data with unknown group classes. The clustering algorithm and neural network are also combined to improve classification accuracy. The main elements of the algorithm proposed in this paper are as follows. First, one-third of a set of unknown group class data is selected as the sample data. In order to accurately assess the characteristics of a set of anonymous group class data, it is important first to choose a sample of the data. A sample of one-third of the total data set should be sufficient to provide a reliable representation of the entire population. This sample should be selected at random in order to ensure that the results of the assessment are as accurate as possible. The number of classes is determined by using hierarchical clustering method on the sample data. Then, the non-hierarchical clustering k-means method is used to classify the sample data. Finally, the classification results are trained as the training items of the neural network, and then the model generated after the training is used to classify the overall data. This paper selects three datasets with different kinds, different numbers of variables, and different amounts of data for the experiments and analysis. This paper presents a comprehensive analysis of three distinct datasets. Each dataset has its own unique characteristics, such as its type, the number of variables, and the amount of data contained therein. By leveraging the properties of these datasets, the experiments, and analysis conducted in this paper will provide valuable insights into the data structures and trends contained within. Furthermore, the results from this analysis will serve as a foundation for further research and experimentation. The experimental results show that the combination of clustering algorithm and neural network algorithm will help to improve the accuracy of data classification and identification effectively. This research provides a new way to accurately and effectively perform data classification. Keywords: hierarchical clustering; k-means; neural network; data classification.
Help Desk Support Ticket and Issue Management

IT companies require methodical approaches to handle the growing volume of customer-reported software issues and service requests. An enormous backlog of unresolved issues has the potential to drastically raise software development and maintenance expenses. An IT company therefore need a clearly defined customer support model. It acts as a link between the client and the business from beginning to end. The support industry also used this to track and record any procedures and solutions used to close tickets. With the Helpdesk Ticketing System, you can use an online platform to solve questions in a web-based environment. To ensure seamless and efficient operation, the user can submit tickets for even the smallest questions. Both within the company and remotely, the ticketing tool is usable. which can be accessed by anyone in organization or end user.
Serverless Deployment Methodologies: Smooth Transitions and Improved Reliability

The advancement of cloud computing has opened doors to serverless architectures, granting developers the freedom to create and launch applications without the burden of server management. This paper explores different approaches to deploying serverless applications, namely All-At-Once Deployment, Blue-Green Deployment, Canary Deployment, A/B Testing, and Shadow Deployment. Each method is thoroughly examined to grasp its approach, advantages, and ideal usage scenarios, offering a detailed roadmap for organizations seeking to enhance their deployment workflows for improved efficiency, dependability, and reduced downtime.
COMPORTAMENTO INFORMACIONAL ESTEREOTIPADO CAUSADO PELAS MÍDIAS SOCIAIS: UM ESTUDO DE CASO DO TIKTOK

This study delves into the phenomenon of stereotyped-information behavior within the realm of social media, with a specific focus on the TikTok platform. Employing a multi-method approach, we aim to unravel the intricate dynamics at play when users engage with content that may reinforce existing beliefs and stereotypes. The prevalence and multifaceted nature of stereotyped-information behavior are illuminated through a comprehensive analysis of user interactions on TikTok. Our findings reveal a compelling pattern: users exhibit a tendency to engage with content that aligns with their pre-existing beliefs. A distinctive feature of TikTok is its visual-centric format, characterized by short, engaging videos. This aspect amplifies the potential for stereotype reinforcement. Visuals and memes, in particular, emerge as powerful tools for the dissemination of both accurate and biased information. Furthermore, our investigation sheds light on the formation of filter bubbles and echo chambers within the TikTok ecosystem. Users tend to be enveloped in content and interactions that reaffirm their existing beliefs, potentially limiting exposure to diverse perspectives. This phenomenon mirrors observations on other social media platforms and emphasizes the need for strategies to foster a more inclusive information environment. This research underscores the pressing need for continued investigation into the ways social media platforms influence the consumption and processing of information. As these platforms continue to shape public discourse, it is imperative that users, content creators, and platform developers alike remain vigilant in their efforts to mitigate the potential perpetuation of stereotypes.
The Influence of The Higgs Domino Online Game on The Learning Outcomes of Students

This study aims to determine the effect of the Higgs Domino Online Game on Student Learning Outcomes at SMAN 10 Jeneponto. The population in this study were 127 students. The research subjects were 96 students of class XII at SMAN 10 Jeneponto, using the simple random sampling technique with the Slovin formula. The research method is quantitative, data was collected using a questionnaire compiled with a Likert scale for each variable, namely the higgs domino online game scale and learning outcomes were tested for validity and reliability. Test the hypothesis with simple regression that has gone through the classic assumption test. Obtained a significance level (P) of 0.000 (p &lt;0.05). This shows that there is a significant influence between the Higgs Domino online game on student learning outcomes at SMAN 10 Jeneponto. Then based on the value of R Square (R^2) it can be said that 34.8% of the variation in learning outcomes obtained is explained by the higgs domino online game of 34.8% in this study.
Analisis Penggunaan Aplikasi Schoology Terhadap Minat Belajar Siswa Kelas XI IPS MA Nurul Jadid Sumber Anyar

This study aims to determine the use of the schoology application on the interest in learning of class XI social studies students. Approach This research uses a qualitative descriptive approach. Respondents in this study were 2 IT (Information Technology) teachers and five students of class XI IPS MA Nurul Jadid from 18 students. Data collection techniques carried out include observation and interviews. Data analysis techniques in this study include data reduction, data presentation, and drawing conclusions. Based on the results of the research and discussion, it can be concluded that the use of the Schoology application can be carried out well in class XI IPS MA Nurul Jadid Sumber Anyar. Analysis of the use of the Schoology application by focusing on student interest in independent character, discipline and responsibility.
Information ethics and the corporatist academic enterprise: critical implications for education of library and information workers

Examines information ethics in the 21st century University and cautions that it is in danger of following the path of information literacy in service of the state. She addresses disturbing trends that influence the type of information ethics education that future library and information workers currently receive in North America, including new managerialism; market values set by students as customers and their market demand for courses; a vocational orientation to train people for jobs without a context of a broader education; civil discourse and respectful workplace policies used in a way to fundamentally threaten academic freedom; growing national security; the race for internationalization; and, the unbundling of academic work. Toni’s words prompt us to discuss the extent to which social responsibility in library and information education can survive repressive tolerance. And she argues for the human condition to feature prominently in our field’s contributions to the future landscape of migrant and digital information labour.
The Effectiveness of Using Flashcard to Improve Students’ Vocabulary Mastery

Students' English language skills are still inadequate, according to preliminary research conducted by the researcher at MTs Salafiyah Syafi'iyah Seblak Diwek Jombang. One of the causes of low student ability is the lack of vocabulary mastery. So, the researcher has taken the initiative to use flashcard media. The purpose of the study was to determine the effectiveness of students' vocabulary mastery before and after being taught using flashcards. The researcher has used a pre-experimental design, consisting of pre-test, treatment, and post-test. The study population was 108 students of class VII MTs Salafiyah Syafi'iyah Seblak Diwek Jombang. The sample was 19 students of class VII-D using purposive sampling. The study population was 108 students of class VII-D MTs Salafiyah Syafi’iyah Seblak Diwek Jombang. The sample was 19 students of class VII-D using purposive sampling. To collect data on students' vocabulary knowledge, the researcher has used pre-test and post-test as research instruments. The test was an objective test, which consisted of 25 multiple-choice questions. The researcher used a t-test to test the data and obtained Sig. (2-tailed) less than 0.05. Then it is stated that Ha has been accepted. This has shown that there is a considerable variation in the average score of students' vocabulary acquisition before and after being taught in the classroom by using flashcards. After collecting the data, the researcher received before and after test results, with a pretest mean score of 74.32 and a posttest mean score of 90.53. Based on the results of the data analysis, many conclusions have been obtained. Namely, before using flashcards, students' vocabulary was lower than after using flashcards. Students' vocabulary before and after teaching using flashcards. Thus, it has been concluded that flashcard media has been useful in the vocabulary of students of class VII D MTs Salfiyah Safi'iyah Seblak Diwek Jombang.
DESIGNING OF LOCATION MONITORING APPLICATION IN THE TANGERANG REGION WHICH IS SHOCKING WATER SOURCES BASED ON ANDROID

Android is an operating system that is specifically designed for smartphones and tablets. This Android system has a Linux base which is used as the basic foundation of the Android operating system. Linux itself is an operating system that is specifically designed for computers. Resources in the form of water that are useful or potential for humans, difficulties in monitoring water sources are an obstacle that is currently felt by the community. With the help of current technological advances, namely IoT (Internet of Things) can reduce the problem that exists today, using only an android smartphone. Android smartphones function to send and receive information via the internet network and also make communication easier. This application can monitor community water sources by obtaining notifications that are in water sources. With this, people can enjoy a healthier life because they can control the cleanliness of water sources easily and practically.
UAV-Based IoT Applications for Action Recognition

The Internet of Things (IoT) concept permits the connection of any device using any network to deliver services. The rapid development of IoT has capitalized the integration of new devices such as Drones which gives bird's eye view. In fact, Drones or Unmanned aerial vehicles (UAV) have seen a tremendous increase in the last few years. It is possible to use UAVs for a variety of tasks, including surveillance, delivery, and search and rescue. It is important in these applications to analyze human behavior from an aerial point of view. Therefore, the number of approaches for analyzing actions from aerial videos has emerged. A number of datasets have been proposed for the development and the evaluation of these approaches. Nevertheless, currently there is no survey devoted to the subject of human action recognition from aerial videos. Hence, we tried to cover the lack of a complete description of the most important aerial dataset and present an overview of the state-of-the-art approaches proposed on this field. The detailed analysis of these datasets and approaches is an essential reference for future research.
Identification of suspicious transactions in the bitcoin network: analysis, features, and machine learning algorithms

This article talks about one of the most popular cryptocurrencies today bitcoin. The principles of bitcoin and blockchain technologies are considered, where the proc and cons of bitcoin are listed. The focus of the research work is on the analysis of the use of cryptocurrencies, namely bitcoin for illegal and criminal purposes. Particular attention is paid to identifying a set of signs to identify suspicious activity in the bitcoin network. During research work reviewed and analyzed many scientific publications and articles, and from these studies, signs were identified that are key attributes when evaluating a Bitcoin transaction for suspicious or questionable transactions. Based on these features, an input dataset of 15 attributes with about 100,000 transactions was generated for further use in building a model that will identify suspicious bitcoin transactions. Using this dataset, models were created and trained to detect suspicious transactions in the bitcoin network based on several machine learning algorithms, such as random forest, logistic regression, k-nearest neighbors, decision tree. The article also provides comparisons of the results of machine learning algorithms, and in addition, selects the best algorithm that showed the best accuracy.
Mobile learning technology in the educational process of a technical university

This article discusses the importance of mobile learning compared to traditional teaching methods. Mobile technologies provide effective interaction for all participants of the educational process, allow organizing collaborative work, effectively distribute resources and provide the necessary tools for solving educational tasks. The authors of this article present their views on the organization of the learning process in a technical university. They found that the use of mobile technologies in the learning process allows focusing on the formation of an educational virtual environment, in which students develop and learn under the guidance of a teacher. The article considers mobile learning as a promising direction for the development of digital education. It describes the advantages and disadvantages of mobile learning in the organization of the educational process, and also reveals the content of mobile applications in the educational process. Examples of mobile learning in modern education are also given. Despite the fact that cell phones are widespread and accessible to students, mobile learning is used less frequently in domestic universities. The article explains the term “mobile learning” and touches on the problems of its use and research in education. To analyze the readiness of learners to use mobile technology in education, a survey of students in the “information technology” program of Zhangir Khan Agrarian and Technical University was conducted. The results of the survey were analyzed. As a result of the survey, it was concluded that the technical and psychological readiness of students to use mobile technology in the educational process of the university requires further study of new opportunities for more effective use of mobile learning technologies.
Approaches to the design of a software system for monitoring and analyzing data of social networks, adapted to the specificity of the kazakh and russian languages

This article is a description of the process of designing a software system for data analysis and monitoring of social networks, adapted to the specifics of the Kazakh and Russian languages. Also, a review of existing systems used by ordinary users, government agencies, commercial organizations, as well as scientific organizations, and designed for the analysis and monitoring of social networks, was carried out.