Reviewerly/exHarmony huggingface dataset:
The Influence of Interactive Method toward the Students’ Speaking Skills at the SMP Santo Thomas 3 Medan

In this study, the writer focused on the use of interactive methods on the speaking skills of class VII students of SMP Santo Thomas 3 Medan with the aims of 1) to determine whether the interactive method has enough influence on the speaking skills of class VII students of SMP Santo Thomas 3 Medan and 2) to find out how enthusiastic are students in learning speaking after they are thought by interactive methods. The type of research used is Classroom Action Research (CAR). The results of this study indicate that: 1) the interactive method affects students' speaking skills, increasing the average score of students' pre-test by 56.03%, formative test by 61.35%, and post-test by 72.32%. 2) It can be seen that the student's response to the interactive method on students' speaking skills is very positive. The percentage of the questionnaire results in statement number 3 there were 23 students 82% who agreed to the statement that students became more enthusiastic about speaking English in learning to speak English using material describing objects using interactive methods, and 4 students 14% strongly agreed. It can be concluded that the application of the interactive method positively influences students' speaking ability, which is the achievement of students' speaking skills.
Influence of Contemporary Educational Qualification on Self-Efficacy: A Study of Madrasa Students

This research is an endeavor to investigate the influence of contemporary educational qualification on self-efficacy among madrasa students. The nature of the research was quantitative. A survey design was applied to gather the data from participants. The population of the study were the students of madrasas in Punjab. 300 madrasa students of darja ʻAma level were selected out of 10 madrasas as a sample of the study. As an instrument, a questionnaire measuring the self-efficacy of students was used which contains 28 items. To ensure the reliability, a pilot study of the questionnaire with 70 madrasa students was conducted. The value of Cronbach’s α was 0.81. The data were analyzed using SPSS 26 in which descriptive and inferential statistics were incorporated. One way ANOVA test was applied for comparison. To further analyze the results of one-way ANOVA, post hoc test was run. Two-way ANOVA was also run to examine the main and interaction effects. Key findings of the research indicated a statistically significant difference in the self-efficacy of madrasa students in different groups with regard to contemporary educational qualification. The findings revealed that, the madrasa students with greater level of contemporary education demonstrated greater levels of self-efficacy in comparison to those with lower level of contemporary educational qualification. However, no significant difference was seen in comparison of graduation level madrasa students with the intermediate level madrassa students. The results of two way ANOVA suggested that there is an impact of contemporary educational qualification and madrassa types and has an interaction between interaction between contemporary educational qualification and madrassa types on self-efficacy.&#x0D; Based on findings, the researcher suggests that the contemporary subjects such as vocational, professional, technological and international languages should be included in the madrasa curriculum. Moreover, training and counselling workshops should be administered to assist madrasa students for overall development including their intellectual, social, emotion well-beings.
Study on the Environmental Classification of High Geothermal Tunnel Based on Worker Health Protection

High-temperature and high-humidity conditions in geothermal tunnels have adverse effects on the well-being of construction workers. This study aims to establish critical environmental thresholds for construction workers in such conditions. The real-time monitoring of workers encompassed continuous assessment of vital parameters, including dry bulb temperature, relative humidity, wet bulb globe temperature (WBGT), skin temperature, and heart rate. Utilizing actual measurements, this research examines how elevated tunnel environment affects human physiological indicators. The hygrothermal environment within high geothermal tunnels is stratified into five categories: comfort zone, general comfort zone, safety zone, heat tolerable zone, and danger zone. We determine thresholds for environmental parameters in each class using mathematical programming. Furthermore, thresholds for discomfort index (DI), heat index (HI), and relative strain index (RSI) heat stress indicators across different classes are derived. The findings indicate that in this high geothermal tunnel construction setting, thresholds for the comfort zone are 26°C for dry bulb temperature, 30% for relative humidity, and 20°C for WBGT index. Safety thresholds are 37°C, 64%, and 32°C, while danger thresholds are 43°C, 81%, and 40°C, respectively. Regarding safety thresholds for heat stress indices (DI, HI, and RSI), they are determined as 24.89°C, 26.9°C, and 0.28°C, respectively. The danger thresholds are 27.76°C, 36.23°C, and 0.39°C, respectively. This investigation provides essential insights to safeguard the health of construction personnel working in high geothermal tunnel conditions.
Interoperability in the Cryptocurrency Ecosystem: A Comprehensive Review and Analysis

The rapid growth of the cryptocurrency ecosystem has led to the proliferation of numerous blockchain networks and protocols, each with its own set of features, tokens, and functionalities. However, the lack of seamless interoperability among these disparate networks presents challenges for the efficient transfer of assets, data, and functionalities across different protocols. This research paper aims to provide a comprehensive review and analysis of the current state of interoperability within the cryptocurrency ecosystem.
LIBRARY AUDIO PODCASTS AS A NEW WAY TO PROMOTE BOOKS AND ENCOURAGE READING IN WEB ENVIRONMENT

The article briefly describes some premises for development of professional interest for Russian modern library specialists’ part to an institutional webcasting, which is considered as promising direction of library communication with internet audience. The authors analyze approaches to creation and promotion of original audio pod-casts, to implementation of own sound digital content demand monitoring with available web analytics tools, to assessment of produced voice tracks efficiency in the context of fostering serviced citizens’ read activities using the examples of tree libraries operating in Barnaul and Slavgorod (Altai Krai, Siberian Federal District, Russia).
IDENTIFICATION OF EMOTIONAL EXPRESSIVENESS OF THE MEMBERS OF THE CHOREOGRAPHIC TEAM BY MEANS OF THEATRICAL ART

The role and value of the means of theatrical art in the field of additional education of children and the creative process are analyzed. The possibilities of using theatrical components that provide conditions for the emotional development of children in the context of additional education are described. Choreographic art is a separate area of additional education that requires the identification of the emotional sphere of the child. The program and methodology for identifying the emotional expressiveness of pupils by means of theatrical art in the process of staging choreographic compositions and producing creative concert projects are presented.
USING DIGITAL EDUCATIONALUNIVERSITY RESOURCES

The article analyzes the digital educational resources of the university based on OSU. I.S. Turgenev. The content of academic disciplines on the Moodle digital platform was studied. An assessment was made of the demand for and student satisfaction with the digital educational space provided by the university.
A Bayesian Networks Approach for Analyzing Voting Behavior

The problem of finding the factors influencing voting behavior is of crucial interest in political science and is frequently analyzed in books and articles. But there are not so many studies whose supporting information comes from official registers. This work uses official vote records in Spain matched to other files containing the values of some determinants of voting behavior at a previously unexplored level of disaggregation. The statistical relationships among the participation, the vote for parties and some socio-economic variables are analyzed by means of Gaussian Bayesian Networks. These networks, developed by the machine learning community, are built from data including only the dependencies among the variables needed to explain the data by maximizing the likelihood of the underlying probabilistic Gaussian model. The results are simple, sparse, and non-redundant graph representations encoding the complex structure of the data. The generated structure of dependencies confirms many previously studied influences, but it can also discover unreported ones such as the proportion of foreign population on all vote variables.
Philosophical aspects of digitalization of education: «anti-digitalization» approach

The article is devoted to the analysis of digitalization of education from the perspective of the «antidigitalisation» approach. The aim of the study is to identify and discuss the problematic philosophical context specific to the «anti-digitalization» approach. The author believes that this approach should be based on the interpretation of digitalization as a tool for destroying the essence of man, as a result of which he can no longer reproduce either himself or the conditions of his existence. Intellectual labor is being replaced by gamification, entertainment, thinking is de-rationalized, and the phenomenon of «digital dementia» is spreading. In turn, speech activity, communication are reduced to interaction with and subordination to machine algorithms, which leads to desocialization. The paper shows the relationship between the contemporary form of digitalization and Neo-Malthusianism and transhumanism as well as the outdated approaches of mechanistic materialism and metaphysical thinking. What becomes the educational problem in the context of digitalization is the destruction of the traditional system of education and pedagogy, the turning of education into a simulacrum, and of its participants — into algorithm-driven objects. In the author’s opinion, the way out of this crisis situation is possible only with a new reception of the Soviet experience in the field of education and the humanistic heritage of the past.
BO-SMOTE: A Novel Bayesian-Optimization-Based Synthetic Minority Oversampling Technique

An oversampling technique balances a dataset by increasing the number of minority samples. It is a common and effective method in imbalanced learning. However, most oversampling methods have randomness in generating minority samples, which would have negative impacts on the prediction performance of subsequent classifiers. This study treats the prediction made by classifiers as a black-box optimization problem. The optimization objective is to improve the classification accuracy of subsequent classifiers for minority samples. The solution of this optimization problem can be regarded as a minority sample that can be and added to the imbalanced dataset. The minority samples are iteratively generated by Bayesian optimization (BO). We determine two valuable intervals for each 1-D continuous variable feature. One is the interval with the densest minority samples. The other is that with the sparsest majority samples distributed among the minority samples. By adjusting the proportion of samples generated in the two areas, the presented algorithm can be flexibly applied to different datasets. In order to reduce the noise that may be caused by the exploration phase of BO, a sample selection procedure is carried out to eliminate the samples that are worse than those generated at the previous iteration. The samples generated in this way are based on the principle of improving the performance of the classifier, thus avoiding the negative effects of randomness. Experimental results via twenty open imbalanced datasets show that the proposed method obtains better results than existing state-of-the-art oversampling models, thus well advancing the important field of imbalanced learning.
Anonymous Blockchain-Assisted Authentication Protocols for Secure Cross-Domain IoD Communications

Due to its diverse and promising use cases, including civilian, military, and commercial, the Internet of Drones (IoD) has quickly become a hot issue in academic and industry circles. Concerns about drone privacy and security are also growing at the same rate. Drones from different flying zones frequently work together in the IoD to accomplish the same mission, necessitating the development of trust among untrusted domains. Recently, some researchers have used blockchain in their authentication scheme to establish trust across different domains. However, the integration led to significant communication, computation and storage overheads and the loss of crucial security features like anonymity or unlinkability. To address these concerns, we develop a cost-effective key exchange protocol for cross-domain IoD communications through the judicious use of the blockchain. Specifically, we employ Chebyshev's chaotic map as the crypto-system, and the ledger keeps only the public keys of the drones, resulting in a significant decrease in storage and computation overheads. The proposed protocol not only complies with the stringent Canetti and Krawczyk adversary model but also satisfies the essential security requirements for IoD, such as anonymity and unlinkability. Using informal justification, formal proof, and automated security reasoning in addition to a comparative performance evaluation, this research substantiates its claims.
A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25\ttimes 25$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$49\ttimes 49$</tex-math> </inline-formula> pixels for blurred images of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$129\ttimes 129$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$257\ttimes 257$</tex-math> </inline-formula> pixels, respectively. Fabricated in 40-nm CMOS, the accelerator’s core area is 3.98 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\text{2}}$</tex-math> </inline-formula> . The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25\ttimes 25$</tex-math> </inline-formula> pixels for an image patch with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$129\ttimes 129$</tex-math> </inline-formula> pixels for deblurring a full-HD image in 1.7 s, achieving a 2562 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> higher normalized energy efficiency.
Causally Enhanced Initial Conditions in Physical Information Neural Networks

Physical Information Neural Networks (PINNs) is a deep learning method for solving partial differential equations. In contrast to data-driven neural network solvers, it can embed physical information into neural networks, this allows the neural network to learn Pdes when fewer labels. There are numerous strategies available to improve the performance of PINNs, including methods such as temporal decomposition and enforcing hard constraints on boundary and initial conditions. However, the use of a surrogate network to impose hard constraints on initial conditions is not universally applicable and can sometimes present challenges in training the neural network. In this paper, inspiration from temporal decomposition and gradient-enhanced PINNs, we introduce the concept of a soft constraint at the initial condition. Moreover, in order to solve the difficulty of loss reduction caused by multiple loss terms, the causal coefficient is introduced as a hyperparameter selection during training. Experimental results demonstrate that this approach represents a more universally applicable method of constraint than surrogate network.
Private Federated Submodel Learning via Private Set Union

We consider the federated submodel learning (FSL) problem and propose an approach where clients are able to update the central model information theoretically privately. Our approach is based on private set union (PSU), which is further based on multi-message symmetric private information retrieval (MM-SPIR). The server has two non-colluding databases which keep the model in a replicated manner. With our scheme, the server does not get to learn anything further than the subset of submodels updated by the clients: the server does not get to know which client updated which submodel(s), or anything about the local client data. In comparison to the state-of-the-art private FSL schemes of Jia-Jafar and Vithana-Ulukus, our scheme does not require noisy storage of the model at the databases; and in comparison to the secure aggregation scheme of Zhao-Sun, our scheme does not require pre-distribution of client-side common randomness, instead, our scheme creates the required client-side common randomness via random symmetric private information retrieval (RSPIR) and one-time pads. Our system is initialized with a replicated storage of submodels and a sufficient amount of common randomness at the two databases on the server-side. The protocol starts with a common randomness generation (CRG) phase where the two databases establish common randomness at the client-side using RSPIR and one-time pads (this phase is called FSL-CRG). Next, the clients utilize the established client-side common randomness to have the server determine privately the union of indices of submodels to be updated collectively by the clients (this phase is called FSL-PSU). Then, the two databases broadcast the current versions of the submodels in the set union to clients. The clients update the submodels based on their local training data. Finally, the clients use a variation of FSL-PSU to write the updates back to the databases privately (this phase is called FSL-write). As the databases at the server do not communicate, as a novel approach, we utilize carefully chosen alive clients to route the required information between the two databases. Our proposed private FSL scheme is robust against client drop-outs, client late-arrivals, and database drop-outs.
Privacy-Preserving Federated Learning With Resource Adaptive Compression for Edge Devices

Federated learning has gained widespread attention as a distributed machine learning technique that offers data protection when training on local devices. Unlike conventional centralized training in traditional machine learning, federated learning incorporates privacy and security measures as it does not share raw data between the client and server, thereby safeguarding potentially sensitive information. However, there are still vulnerabilities in the FL field, and commonly used approaches, such as encryption and blockchain technologies, often result in significant computational and communication costs, making them impractical for devices with restricted resources. To tackle this challenge, we present a privacy-preserving federated learning system specifically designed for resource-constrained devices, leveraging compressive sensing and differential privacy techniques. We implemented the weight-pruning-based compressive sensing method with an adaptive compression ratio based on resource availability. In addition, we employ differential privacy to introduce noise to the gradient before sending it to a central server for aggregation, thereby protecting the gradient’s privacy. Evaluation results demonstrate that our proposed method achieves slightly better accuracy when compared to state-of-the-art methods like DP-FedAvg, DP-FedOpt, and AGC-DP for the MNIST, Fashion-MNIST, and Human Activity Recognition datasets. Furthermore, our approach achieves this higher accuracy with a lower total communication cost and training time than the current state-of-the-art methods. Moreover, we comprehensively evaluate our method’s resilience against poisoning attacks, revealing its better resistance than existing state-of-the-art approaches.