Reviewerly/exHarmony huggingface dataset:
Dynamics Reflects Quantum Phase Transition of Rabi Model

As the simplest and most fundamental model describing the interaction between light and matter, a breakdown in the rotating wave approximation of the Rabi model leads to phase transition versus coupling strength when the frequency of the qubit greatly surpasses that of the oscillator. In addition to the phase transition revealed in the ground state, we show that the dynamics of physical quantities can reflect such a phase transition for this model. In addition to the excitation of the bosonic field in the ground state, we show that the witness of inseparability (entanglement), mutual information, quantum Fisher information, and the variance of cavity quadrature can be employed to detect the phase transition in quench. We also reveal the negative impact of temperature on checking the phase transition by quench. This model can be implemented using trapped ions, superconducting artificial atoms coupled bosonic modes, and quantum simulations. By reflecting the phase transition in a fundamental quantum optics model without imposing the thermodynamic limit, this work offers an idea to explore phase transitions by nonequilibrium process for open quantums.
A-GSTCN: An Augmented Graph Structural–Temporal Convolution Network for Medication Recommendation Based on Electronic Health Records

Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide a reasonable prescription for patients according to their historical and current health conditions. However, the existing recommended methods have many limitations in dealing with the structural and temporal characteristics of EHRs. These methods either only consider the current state while ignoring the historical situation, or fail to adequately assess the structural correlations among various medical events. These factors result in poor recommendation quality. To solve this problem, we propose an augmented graph structural-temporal convolutional network (A-GSTCN). Firstly, an augmented graph attention network is used to model the structural features among medical events of patients' EHRs. Next, the dilated convolution combined with residual connection is applied in the proposed model, which can improve the temporal prediction capability and further reduce the complexity. Moreover, the cache memory module further enhances the model's learning of the history of EHRs. Finally, the A-GSTCN model is compared with the baselines through experiments, and the efficiency of the A-GSTCN model is verified by Jaccard, F1 and PRAUC. Not only that, the proposed model also reduces the training parameters by an order of magnitude.
Identifying key features of digital resources used during online science practicals

As in our everyday lives, we use digital elements as part of formal and informal education. To serve their educational purpose well, systematic research is desirable to identify and measure their characteristics. This study focuses on science practicals, which are complex and vary in organizational settings and specific arrangements, including usage of digital elements. We describe the digital resources on which the online instruction of science practicals during the COVID-19 forced lockdowns was built, and their key characteristics were identified. Data were collected from science teachers in Slovakia, Czechia, Slovenia, France, and Spain. The teachers shared the web resources they used and that they would recommend, together with a description of the resources. We recorded 89 inputs representing 50 unique resources. Teachers preferred free resources, mostly for knowledge revision, and newly discovered 36% of them due to forced distant teaching. The best evaluated resources were those supporting interaction (especially among peers), focused on teaching subjects and/or ICT, ready to use, and with a clear structure. The resource most frequently mentioned and used in more than half of the countries was PhET (Interactive Simulations for Science and Math) which provides free simulations of scientific principles. Other characteristics mentioned in the literature (e.g., supporting creativity and independent solving, connecting different levels of organization, authenticity, flexibility) were not that important for the overall rating.
Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead

The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely performed in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between state-of-the-art computer vision algorithms and the successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on IoT edge streaming video analytics (IE-SVA) systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of IE-SVA systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short-, medium-, and long-term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the evolution of IE-SVA systems.
A Learning Analytics Architecture for a Smart University

In this scholarly chapter, an architecture of the big data-enabled learning analytics framework in a smart university is developed. The objective of the architecture is to make an attempt to seamlessly integrate the generation, acquisition, cleaning or pre-processing, storage management, analytics, visualization, and alerts in a learning analytics in a smart university. The architecture is tailored for a university with advanced technologies such as artificial intelligence. Smart universities can use learning analytics to boost student learning, optimize educational processes, and make data-informed decisions by understanding the essential elements, data flow, and technology involved. The chapter also discusses the benefits, challenges, and ethical considerations associated with implementing learning analytics in smart university environments.
Restructuring of Universities in the Middle Volga Region and New Forms of Work Organization in the 1930s

This article examines the changes in the higher education system of Russia during the 1930s when the long-established educational system was transformed to meet the state’s demands. The Soviet state faced the problem of training and fostering highly qualified specialists to solve the problems of the country’s industrialization. For this reason, an extensive campaign was launched to create technical universities in the country. The Middle Volga region, where several technical universities were opened in a short time, played an important role in this process. Based on archival documents, the steps taken to build a new higher school are analyzed. Due to the state demand for the economy industrialization, new forms of work organization were introduced into the professional activities of professors and lecturers, in some cases either against their actual will or under pressure from the authorities. A close connection between science and production contributed to the emergence of new work formats that did not always “fit” into the academic environment. By the end of the 1930s, however, the Soviet state, which had set a clear course for the development of industrialization, defined technical specialization as a priority direction in the system of higher education.
On a variant of Pillai’s problem with factorials and S-units

Abstract Let S be a finite, fixed set of primes. In this paper, we show that the set of integers c which have at least two representations as a difference between a factorial and an S -unit is finite and effectively computable. In particular, we find all integers that can be written in at least two ways as a difference of a factorial and an S -unit associated with the set of primes $$\{2,3,5,7\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:mn>5</mml:mn> <mml:mo>,</mml:mo> <mml:mn>7</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> .
Enhancing question answering in educational knowledge bases using question-aware graph convolutional network

In this paper, a question answering method is proposed for educational knowledge bases (KBQA) using a question-aware graph convolutional network (GCN). KBQA provides instant tutoring for learners, improving their learning interest and efficiency. However, most open domain KBQA methods model question sentences and candidate answer entities independently, limiting their effectiveness. The proposed method extracts description information and query entity sets for a specific question, processes them with Transformer and pre-trained embeddings of the knowledge base, and extracts a subgraph of candidate answer sets from the knowledge base. The node information is updated by GCN with two attention mechanisms expressed by the question description and query entity set, respectively. The query description information, query entity set, and candidate entity representation are fused to calculate the score and predict the answer. Experiments on MOOC Q&amp;A dataset show that the proposed method outperforms benchmark models.
Blockchain-Based Cybersecurity in Internet of Medical Things (IoMT)-Based Assistive Systems

Today's internet of medical things (IoMT) devices has revolutionized healthcare by enabling the development of assistive systems that enhance patient care and improve healthcare outcomes. However, the increasing connectivity and data exchange in IoMT systems raise issues about cybersecurity and patient data privacy. This chapter explains the application of blockchain-based cybersecurity in IoMT-based assistive systems. Blockchain technology offers a decentralized and immutable ledger that ensures the integrity, security, and transparency of data in IoMT systems. By adding blockchain, cybersecurity challenges in IoMT can be addressed effectively (with providing several benefits, including enhanced data protection, secure identity management, and robust access control mechanisms). Generally, in a blockchain-based cybersecurity framework for IoMT, patient health data is stored in encrypted form on the blockchain, preventing unauthorized access/ tampering. Smart contracts, programmable blockchain protocols, can automate access control and consent management, etc.
Time-weighted motion history image for human activity classification in sports

Abstract Vision-based human activity classification has remarkable potential for various applications in the sports context (e.g., motion analysis for performance enhancement, active sensing for athletes, etc.). Recently, learning-based human activity classifications have been widely researched. However, in sports scenes in which more detailed and player-specific classifications are required, this is a quite challenging task; in many cases, only a limited number of datasets are available, unlike daily movements such as walking or climbing stairs. Therefore, this paper proposes a time-weighted motion history image, an effective image sequence representation for learning-based human activity classification. Unlike conventional MHI based on the assumption that “the newer frame is more important,” our method generates importance-aware representation so that the predictor can “see” the frames that contribute to analyzing the specific human activity. Experimental results have shown the superiority of our method.
Personalized agricultural knowledge services: a framework for privacy-protected user portraits and efficient recommendation

Abstract In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. This paper presents a comprehensive framework that addresses the issues of vague user positioning, serious privacy leakage, and low efficiency in personalized knowledge services within the national agricultural knowledge intelligent service cloud platform. The proposed framework utilizes privacy-protected user portraits based on generative adversarial nets (GAN) and leverages the TextCNN-LSTM algorithm for agricultural knowledge service prediction. By embedding labels into the algorithm and employing data obfuscation techniques, the framework achieves accurate inference of user behavior while preserving user privacy. Experimental results demonstrate the effectiveness and accuracy of the proposed framework, highlighting its potential for regional precise positioning and recommendation of personalized agricultural knowledge services. Experimental data shows that the average absolute error and root-mean-square error of this method are 1.1997 and 1.4143, respectively, and compared with MLP, TextCNN, and LSTM models, and it has higher prediction accuracy. In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services.
Efektivitas Penggunaan Game Kahoot terhadap Hasil Belajar Siswa Mata Pelajaran PAI XI SMAN 1 Lembang Jaya

This research is motivated by the current learning atmosphere which has not been able to motivate students well, resulting in student learning outcomes that are not yet optimal. This is because the exercises in the learning process are still not varied enough, one example of training that can be used is Kahoot. This research aims to determine the effectiveness of using the Kahoot quiz game on the learning outcomes of class IX students at SMA Negeri 1 Lembang Jaya. This research uses a quantitative approach with the experimental method "post test only control group design". The population in this study were all class IX students of SMA Negeri 1 Lembang Jaya. The research sample consisted of 23 students of class XI IPS as the experimental class and 23 students of class The test results were compared using the t-test formula to determine the effectiveness of using the Kahoot quiz game. Based on the t-test results, it was concluded that learning using Kahoot was significantly effective for PAI learning for class XI students at SMA Negeri 1 Lembang Jaya. The results of effectiveness testing using an effect size of 1.814 show that the effect of using the Kahoot online game is high (d=1.814&gt;0.8). This indicates that the use of Kahoot has a significant positive impact on improving student learning outcomes in class XI PAI subjects at SMA Negeri 1 Lembang Jaya.
Development of an Industrial Safety System Based on Voice Assistant

Currently, there are limitations in the human–machine interfaces (HMIs) used in industry, either due to the characteristics of users’ cognitive abilities or interfaces, which hinder communication and interaction between humans and equipment. For this reason, this work presents an alternative interaction model based on a voice assistant, Alexa, which promotes more natural, intuitive, direct, and understandable communication. The purpose of this work is the development of an industrial safety system for a controlled electric motor based on Alexa voice assistant, which allows the monitoring of its operating parameters, such as phase current, housing temperature, and rotor vibration, as well as making it possible to control ignition and shut down and change the rotation of the motor with a prior password, as a safety measure. Commercial smart devices and Arduino-compatible modules were used to achieve this, providing them with the Internet of Things (IoT) feature. In addition, several software platforms, such as Blynk, Tuya Smart, Node Red, and Voiceflow, are used to perform data transmission, device management, and programming of the Alexa skill, oriented to the execution of the security and run system. This shows the potential capacity of voice assistants in the industry to deliver information more naturally to humans and obtain optimal notifications. However, problems were evidenced, such as the influence of noise in the environment when communicating with the assistant, the vocalization of words, low voice tones, and accents typical of the language, that will increase the security level of the system and prevent potential identity theft.
Social Network Sentiment Analysis Using Hybrid Deep Learning Models

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).
Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w. The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization problems. Competitive coevolution disintegrates the problem into multiple sub-problems, and these sub-swarms coevolve for a better solution. The best solution is selected and replaced with the current sub-swarm for the next competition. This process increases population diversity, reduces premature convergence, and increases the memory efficiency of PPSO. Simulation results using PPSO, fuzzy-dominance-based many-objective particle swarm optimization (FMPSO), and improved competitive multi-swarm PPSO (ICPPSO) are generated to assess the convergence power of the proposed algorithm. The experimental results show that ICPPSO achieves a dominating performance. The ICPPSO results for the average fitness show average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO. The Wilcoxon statistical significance test also confirms a significant difference in the performance of the ICPPSO, PPSO, and FMPSO algorithms at a 0.05 significance level.
Performance Evaluation of Data Utility for a Differential Privacy Scheme Supporting Fault Tolerance

The evolution of smart grids improves the sustainability, controllability, stability, and efficiency of traditional power grids. There is a challenging issue in smart grids with protecting users’ privacy while collecting and controlling individual fine-grained data. To ensure data integrity and address the privacy issue, differential privacy protection is an efficient method to resist differential attacks on aggregated data. However, due to differential noise and faulty smart meters, the problem of differential noise deviation has a great impact on the utility of aggregated data. In this paper, we further supplement the previous work by improving the prediction method, forming a relatively complete DP protection scheme (DPP-UFT) with fault tolerance, and providing a detailed performance evaluation process. The experimental results show that the proposed method of adding differential noise based on the estimated failure rate is related to the estimated failure rate and the noise factor. Compared with several other related literature, it has achieved a higher data utilization effect.
PECULIARITIES OF ORGANIZING BLENDED LEARNING IN HIGH SCHOOL MATHEMATICS LESSONS

The article is devoted to the study of the peculiarities of organizing blended learning in high school mathematics lessons. The author characterizes the combination of traditional methods and modern digital technologies in blended learning, which provide students with the opportunity to actively engage in the process of learning. Various aspects of the organization of blended learning in mathematics lessons are formulated. The use of interactive electronic resources to improve students' understanding of mathematical concepts is investigated.The influence of the blended learning approach on students' learning outcomes, their motivation and independence is demonstrated. The role of the teacher as a mediator in blended learning and methods for engaging students in working with digital communications are considered. The importance of blended learning in high school mathematics classes is revealed and tips for the optimal organization of such an educational process are highlighted. The potential challenges associated with the organization of blended learning in high school mathematics lessons are outlined: the need to establish access to digital resources and the Internet, teacher support with information technology, tracking student progress and feedback. The advantages of blended learning are presented: individualization, greater student engagement, more flexible scheduling, etc.Tips for the optimal organization of blended learning in high school mathematics classes are highlighted. The author emphasizes the need to establish access to digital resources and the Internet, as well as to support teachers with information technology. An important aspect is also tracking students' progress and providing feedback to support their learning.In general, the article emphasizes the importance of blended learning in high school mathematics classes and its positive impact on the quality of learning and student development. Integration of digital technologies helps to create a stimulating learning environment, promotes active participation of students in the learning process and develops their knowledge and skills. However, it is important to keep in mind the potential challenges that may arise when organizing blended learning and to provide adequate support for the successful implementation of this educational process.
The Past, the Present, and the Future of the Evolution of Mixed Reality in Teacher Education

The authors in this article provide a historical view (past) on the development of mixed reality (MR) simulation in teacher education as well as a brief history of simulation from other fields along with foundational knowledge on the evolution of simulation. The authors provide a systematic review of the current state (present) of the research in MR for teacher education within the past 5 years aligned with the research question “What are the uses, practices, and outcomes of MR simulation in teacher preparation?”. Three themes were identified, i.e., simulation to this point is designed by teacher educators, feedback matters in impacting outcomes, and practice is safe and reflective for those who prepare teachers in these environments. A summary is provided of these key articles and the findings. The authors conclude the article by sharing the potential evolution (future) of aspects of the model of MR, focusing on the use of AI agents and multi-modal data collection, including biometric signals, providing insights into simulation in teacher education.