Reviewerly/exHarmony huggingface dataset:
A systematic survey for precursory tilt changes at all monitored eruptions in Japan

Volcanic eruptions are sometimes preceded by tilt changes that are an indicator of immediate pre-eruptive processes. In this study, the tilt changes before all monitored eruptions in Japan were surveyed for the first time to address the generality and common features of these signals. An automated procedure was devised to identify tilt changes. The results showed that approximately half of the analyzed eruptions were preceded by tilt changes. Detection ratios depended on station locations, and approximately 10–20% of the total waveforms consisted of detectable tilt changes. Broadband seismic records, when integrated into displacements, showed detection ratios comparable to those of tiltmeter records. The lead times and durations of the tilt changes showed large variations. The durations correlated with amplitudes, although with considerable scatter. Both accelerating and decelerating types of tilt changes were common, and their ratios depended on the volcanoes. These are the first findings on the nature of pre-eruptive tilt changes from many volcanoes, and the waveform analysis procedure proposed in this paper is applicable to other regions. Future works involve extending this study to datasets from other regions, detecting tilt changes from continuous records, applying the proposed method to an early warning system, and detecting longer tilt changes, which require that the proposed approach be improved by subsequent studies.
Developing deep knowledge in middle school mathematics: a textbook for teaching in the age of technology

Click to increase image sizeClick to decrease image size AcknowledgementsThe authors wish to extend their gratitude to Pusat Layanan Pembiayaan Pendidikan (PUSLAPDIK) and Lembaga Pengelola Dana Pendidikan (LPDP) for providing support in the publication of this article.
A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization

Solar irradiance is a crucial environmental parameter for optimal control of photovoltaic (PV) systems. However, precise measurements of the solar irradiance are difficult since the irradiation sensors (i.e., pyranometer or pyrheliometer) are expensive and hard to calibrate. This paper proposes a cost-effective and accurate method for estimating the solar irradiance with a PV module via curve fitting. A dual-mode Jaya (DM-Jaya) optimization algorithm is introduced to extract the real-time value of solar irradiance from the measured PV characteristics data by using two search strategies. The step sizes of a random walk are taken from even and Lévy distribution distributions in different searching phases. Compared with the traditional irradiance sensors, the proposed estimator does not require additional circuit and obtains relatively lower error rates. A comparative study of seven population-based optimization algorithms for the optimal design of the estimator is presented. These algorithms include particle swarm optimization (PSO), cuckoo search (CS), Jaya, simulated annealing (SA), genetic algorithm (GA), supply-demand-based optimization (SDO), and the proposed DM-Jaya algorithm. Simulations and experimental results reveal that DM-Jaya outperforms the other optimization algorithms in terms of the estimation speed and accuracy.
Metaphysics of Progress and the Disciplinary Structure of Science

This text represents comments to the article by T.D. Sokolova “Conceptualization of scientific progress. A Case of Historical Epistemology”. Her article combines the concepts of differentiation and integration of scientific knowledge, on the one hand, and the concept of the progress of science interpreted by the historical epistemology of G. Bachelard, on the other, which ultimately is designed to solve the “paradox of interdisciplinarity” and the problem of disciplinary separation of scientific knowledge. The problem posed is highly significant for understanding modern science. At the same time, its formulation can be elaborated and even reformulated, and its premises clarified, since they themselves are the subject of extensive discussions. To do this, it is necessary to revise the widely known definitions of interdisciplinarity and transdisciplinarity in such a way as to detect the value-worldview content relevant to science as a socio-cultural system that includes the idea of progress.
Developing an Augmented Reality-Based Interactive Learning System with Real-Time Location and Motion Tracking

This study aims to develop an interactive learning solution for engineering education by combining augmented reality (AR), Near-Field Electromagnetic Ranging (NFER), and motion capture technologies. We built an instructional system that integrates AR devices and real-time positioning sensors to improve the interactive experience of learners in an immersive learning environment, while the motion, eye-tracking, and location-tracking data collected by the devices applied to learners enable instructors to understand their learning patterns. To test the usability of the system, two AR-based lectures were developed with different difficulty levels (Lecture 1 - Easy vs. Lecture 2 - Hard), and the System Usability Scale (SUS) was collected from thirty participants. We did not observe significant usability difference between Lecture 1 and Lecture 2. Through the experiment, we demonstrated the robustness of this AR learning system and its unique promise in integrating AR teaching with other technologies.
WebAR-NFC to Gauge User Immersion in Education and Training

Augmented Reality (AR) is a promising technology for enhancing engagement and understanding in education and training. However, the challenge of measuring user immersion and engagement in AR remains a significant barrier to its widespread adoption. This research proposes a new WebAR-NFC system with hand tracking to measure user immersion and engagement in educational settings. The system integrates WebAR content with NFC tags and hand-tracking technologies to enable the measurement of user interactions with educational content. The proposed system addresses the limitations of previous methods for measuring user immersion in AR, such as self-report or behavioral measures, by providing a more objective and accurate measurement of user engagement. Hand-tracking in the system offers a unique measure of user interactions with educational content that is not available with previous methods. The system’s potential to improve learning outcomes by enhancing user engagement and immersion suggests it may have significant practical applications in education and training. The system’s accessibility through a web browser on mobile devices or computers makes it widely available to learners, making it suitable for various educational settings. The research proposes a novel methodology for measuring user immersion and engagement in AR, which can significantly contribute to the development and adoption of AR in education and training. The results of this research can advance the understanding of user engagement and immersion in AR, providing insights into how to design effective AR systems for education and training.
Learning Agile Estimation in Diverse Student Teams by Playing Planning Poker with the Humanoid Robot NAO. Results from Two Pilot Studies in Higher Education

Agile estimation is performed by teams to predict relative effort needed to finish project tasks. Estimating in diverse teams may be challenging due to different subjective perspectives. Planning poker is a game-based technique applied by agile teams to empower all team members to jointly estimate and reach a consensus about the predicted effort. This paper presents results from two studies on learning agile estimation in which student teams were supported by the humanoid robot NAO acting as facilitator of the game. The paper describes the design of the robot-assisted planning poker simulation, the programming of the application, and the evaluation results from two pilot studies with 29 bachelor and master students in different study programs and with different cultural backgrounds. The evaluation aimed to investigate students' perceptions of the robotic facilitator, self-assessment of learning outcomes related to agile estimation, and possible effects of different cultural dimensions on the perceptions of the robot-assisted simulations and the learning outcomes. The results show that both bachelor and master students, independent of their cultural background, perceived the NAO robot as a trustworthy, friendly and likeable facilitator of the planning poker game. Students with a European background rated the possibility of befriending NAO slightly higher compared to non-European students. Students also reported that playing planning poker in teams with support of the robot helped them to understand agile estimation. The participants recommended to use the simulation game “Planning poker with NAO" in future classes on project management and estimation.
Study on VR Science Educational Games Based on Participatory Design and Flow Theory

By exploring the possibilities and scientificness of Participatory Design (PD) for children, children can be taught as well as the recipients in the process of collaborative game design. High-tech Virtual Reality (VR) technology can be used to develop popular science games and improve educational experience. Participate in the whole process across the world, obtain the principles, strategies, modes and visual styles of popular science games through PD method, build the process framework of educational game design, use VR technology to enhance user’s sense of immersion, sense of cognition and sense of interaction, and complete the development by combining Unity3D engine. The Mental Flow (MF) evaluation model was applied to the test of the game, and the quantitative evaluation model was established. Finally, a set of PD strategies and MF experience testing and evaluation methods for VR popular science educational games are produced, and theoretical basis and practical reference are provided for the development and design of future popular science educational games. PD concept can provide users with real “people-oriented” design output, “edutainment” games can mobilize users to learn knowledge initiative and fun, VR technology for users to create immersive experience, has a positive impact on the MF experience.
Educational Effect of Molecular Dynamics Simulation in a Smartphone Virtual Reality System

Students have difficulty understanding complex molecular structures and chemical bonds in a two-dimensional media such as textbooks and writing on the blackboard. Teachers also use molecular models or viewers to teach, but the cost of using them is not negligible. To easily share the molecular images of experts and enhance the understanding and motivation of novices, we have developed a prototype of a standalone smartphone virtual reality (VR) interface with a molecular dynamics (MD) simulator for chemical education, called VR-MD. In this application, users can touch, and move molecules whose coordinates are updated in real time by the MD engine. For teachability and safety, we adopted handheld VR glasses and a VR/augmented reality (AR) mode changer responsive in the state of the user’s hand. As a demonstration experiment, we conducted on-site lectures at a high school with one of these applications was provided for each student. A 7-point scale and a free-response questionnaire were administered following the lecture. The results confirmed the effectiveness of the program for improving comprehension and the motivation to learn.
A graph embedded in graph framework with dual-sequence input for efficient anomaly detection of complex equipment under insufficient samples

Real-time anomaly detection is essential for the safe launch of some sophisticated equipment, such as liquid rocket engines (LRE), in order to head off disasters. However, the Industrial Internet of Things (IIoT) edge's real-time requirements cannot be addressed by the present methodologies, and the outcome is poor when dealing with a lack of training samples on the device side. We provide a solution for device-side real-time anomaly detection using the architecture known as Graph Embedded in Graph Networks to address these issues (GG-Nets). To fully extract features under insufficient training data, in our method, we learn the temporal relationship of timestamps in the multivariate signal through a time-series graph attention network (T-GAT) and extract features, and use the extracted features to replace the original signal as the information of the sensor attention network (S-GAT) nodes. For efficiency, the signals in the original sample are divided into odd and even sequences, which greatly reduces the number of nodes in the T-GAT. Experiments reveal that the proposed method outperforms other state-of-the-art models on the LRE ignition dataset. Furthermore, the ablation experiment proves that each module of the model improves the effect and extensive discussions explore interpretability. Our code can be found on: https://github.com/yhd-ai/GG-Nets.
Attraction Recommendation Based on Tourism Context Modeling and <scp>Multi‐neural</scp> Collaborative Filtering Algorithm

The key to generating tailored suggestions in the scenic location recommendation scene is how to model the diverse tourism environments in order to correctly acquire visitor preferences and scenic spot tourism features. However, most existing recommendation algorithms focus on the spatiotemporal background modeling of historical tourism trajectories. Only tourists' preferences are understood, and the rich heterogeneous tourism information such as tourists' personal tourism constraints and scenic spots' tourism attributes are ignored. In this paper, we proposed a multiple neural collaborative filtering attraction recommendation architecture (MNCF‐AR). To begin, we learn the tourism feature representation of tourists by modeling diverse tourism contexts, and then we create the tourism trajectory background of tourists using a large number of actual tourism logs to achieve the entire feature representation of tourists. Second, to learn the feature vector of scenic spots in the context, the tourism heterogeneous network map is used to build the scenic spot attribute background, the self‐attention network to learn the scenic spot sequence learning, and a neural network to project each scenic spot into a unified potential feature space. Finally, the multi‐neural collaborative filtering method is utilized to forecast the difference in scores between visitors and scenic sites so that tailored scenic spots may be recommended. Extensive experiments on mainstream datasets, such as MaFengwo, New York, Tokyo and Xi'an, show that the proposed method can effectively and accurately recommend attractions for users. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
The Positive Impacts of the Development of New Media on Markets and Enterprises

Nowadays people use mobile phones more and more frequently, the media also advanced a lot. As the new media gets gradually popular, it is essential to know its functions and make use of it in business, making an unprecedented profit. The main target of the passages is to determine how much profit the new media bring to the social economy. Using the way of data analysis, this paper compares the new media with the old media, and we find how does new media contribute to the economy. The result shows that new media has become an indispensable part of human lives and the new media brings economic opportunities to the new generation.
Research on the Relationship between Undergraduate Major and Income

With the increase of the number of college graduates, the scale has gradually exceeded 10 million. With the influence of external environmental factors, more and more college students are facing employment problems, which has become the focus of social attention. This paper mainly uses the method of data analysis to explore the changes in the income of Chinese college students in the past five years. The researcher will discuss the differences in professional income caused by different factors such as professional division, year, university level, gender and region. Based on the project data of MyCOS's "MyCOS 2021 Employment Report for Chinese College Students," this research conducts an empirical analysis of the influencing factors of the monthly starting salary income of undergraduates. The results show that gender, major category, university level, gender and university location all affect the monthly income of college students six months after graduation to some extent.
DEVELOPMENT OF WEBSITE CREATION SKILLS AMONG STUDENTS OF THE TRAINING AREA 51.03.06 LIBRARY AND INFORMATION ACTIVITIES

A modern library specialist is a highly qualified specialist who is proficient not only in traditional library technologies and techniques, but also equally able to use information technologies, including web technologies. The paper discusses the methodology of teaching the discipline «Site Building», describes the practice of teaching it at the Orel State Institute of Culture for the direction of training 51.03.06 Library and information activities. The article analyzes topical issues of the use of web technologies in the professional activity of a librarian.
THE USE OF MODERN COMPUTER TECHNOLOGIES IN THE PROCESS OF PREPARING FUTURE TEACHERS TOWARDS THE FORMATION OF A NATIONAL SELF-AWARENESS OF SCHOOL CHILDREN

The purpose of the article is to consider the essence of national self-consciousness, its structure. The possibilities of using modern educational computer technologies (virtual excursions, online excursions, promotional videos of proposed excursions, etc.) to prepare future teachers for the formation of national self-awareness of students are considered.
DIGITAL TRANSFORMATION OF EDUCATION AND MODERN TRENDS IN THE PREPARATION OF HUMAN RESOURCES

The scientific article presents analytical aspects of the constant transformation ofprofessional training under the influence of modern technological structures, changing labor market requirements and economic demands. Currently, the focus of attention is on the search for the most effective ways to transform educational institutions, the activation of the use of innovative pedagogical techniques aimed at forming not only the professional vision of the graduate, and mastering professional skills, but also acquiringflexible skills to implement the tasks set in conditions of a high level of digitalization of all sectors of the economy and spheres of society.
CURRENT PROBLEMS OF BUILDING CONFLICT-FREE INTERACTION AMONG STUDENTS IN THE EDUCATIONAL PROCESS AND WAYS TO OVERCOME THEM

The article deals with the problems of interaction between students, students and teachers during the educational process, building a conflict-free relationship. The task of pedagogical activity is a purposeful work on learning, development and socialization ofpersonality, transfer of cultural experience. For this purpose, the most favourable socio-psychological conditions should be created. To this end, possible ways of overcoming contradictions and tensions are explored.
Expectation Maximization

The existence of latent variables increases the difficulty of estimating model parameters. The expectation maximization (EM) algorithm is a common method used to obtain the local optima of these parameters of probabilistic graphical models with latent variables. This chapter introduces how to apply the EM algorithm in topic models such as PLSA. The foundation of EM algorithm is introduced in Sect. 4.1. The convergence of EM is introduced in Sect. 4.2 and the generalized expectation maximization (GEM) is discussed in Sect. 4.3. Finally, the applications of EM and GEM in topic models are explained in Sect. 4.4.
Variational Inference

Variational inference has become an important research topic in machine learning. It transforms a posterior reasoning problem into an optimization problem and derives a posterior distribution by solving the optimization problem. The fundamental principles of variational inference are introduced in Sect. 6.1. Then, in Sects. 6.2 and 6.3, we discuss evidence lower bound and the mean field variational inference. In Sect. 6.4, we use LDA as an example to show how variational inference is applied to topic models.
Parameter Setting

Training high-quality topic models depend on appropriate parameter setting. The parameters in topic models fall into two major categories: hyperparameters and the number of topics. We introduce how to set the hyperparameters in Sect. 8.1, the number of topics in Sect. 8.2, and the evaluation of topic models in Sect. 8.3.
Topic Models

Topic models have attracted substantial attention from academia and industry. Researchers have designed various types of topic models that are applied to a wide range of tasks. In this chapter, we select some representative topic models for introducing the mathematical principles behind topic models. By studying this chapter, readers will have a deep understanding of the foundation of topic models, and the ability to select appropriate existing models or design brand-new models for their own scenarios.
Topic Deduplication and Model Compression

There are two critical issues when using the trained topic models: the redundancy of topics and the high computational costs for topic inference. Therefore, there is still room for further optimizing the trained topic models. In this chapter, we introduce topic deduplication and model compression.
Distributed Training

Discovering long-tail topics from massive data usually requires a large number of topics and a large-scale vocabulary. However, using a single machine to train such large-scale topic models encounters bottlenecks in computing efficiency and data storage. Therefore, it is necessary to develop distributed training mechanisms for topic models. In this chapter, we introduce distributed computing architectures in Sect. 7.1, followed by the distributed sampling algorithm in Sect. 7.2, and the distributed variational inference in Sect. 7.3.
Conclusions

This concludes this book on functional programming in R. You now know all the basic programming patterns used in day-to-day functional programming and how to use them in the R programming language.
Introduction

Welcome to Functional Programming in R 4. I wrote this book to have teaching material beyond the typical introductory level most textbooks on R have, where functions are simple constructions for wrapping up some reusable instructions that you can then call when you need those instructions run. In languages such as R, functions are more than this. They are objects in their own right that you can also treat as data, create and manipulate and pass around like other objects, and learning how to do this will make you a far more effective R programmer.
The Populist Voter: A Machine Learning Approach for the Individual Characteristics

Populist parties recently have shaken Western democracies, yet there is no consensus regarding the characteristics of populist voters. By using large-scale surveys from four European countries (France, Germany, Spain, and the U.K.), we investigate individual determinants of populist voting. Our methodological approach controls for model uncertainty by considering the responses to 100 questions that span social, economic, political, environmental, and psychological dimensions. We also include individual misperceptions across several domains. Our results show that left-wing populist voters are not religious, have lower misperceptions regarding foreign-national prisoners, distrust the police, are open to immigrants from poorer countries, and oppose dismantling the welfare state. The right-wing populist voters oppose incoming, racially diverse immigrants, distrust national and international institutions, and have high misperceptions regarding immigrant crimes and the share of social benefits in the GDP. Contrary to the previous literature, attitudes toward globalization, personality traits, labor-market status, and social media use are not consensus variables for either group.