Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Synchronous and Asynchronous Blended Teaching Plan for Preschool Education Major Students at The Institute of Teacher Education in Ningxia Province, People Republic of China

Background and Aims: In recent years, the role of choral conducting courses in China's basic education has been receiving more and more attention. A sudden increase in the demand for chorus and conducting courses in primary school has led to a shortage of teachers in this professional field in underdeveloped areas such as Ningxia. To efficiently train more primary school teachers who possess choral conducting skills for basic education has become the primary focus of colleges and universities offering primary school education majors. The purpose of this article is to explore whether synchronous and asynchronous blended teaching plans can achieve, or even surpass, the academic performance of traditional educational methods while enhancing the efficiency of the curriculum. Methodology: This quasi-experimental study aims to investigate the effects of various teaching plans on participants’ outcomes. The participants consist of students engaged in a chorus and conducting an education setting. In this research procedure, the 39 participants were divided into three groups, each experiencing a different teaching method: the traditional teaching plan group, the synchronous blended teaching plan group, and the asynchronous blended teaching plan group. After implementing the teaching plans, standardized tests are administered to all three groups to measure their learning outcomes and performance. The collected data, comprising participants' test scores, will undergo statistical analysis using ANOVA and the Paired T-test. Result: The final research results indicated that there was no statistically significant difference in the performance of the three participant groups. In other words, all three teaching plans exhibited a positive effect on participants' performance, with similar degrees of improvement. When considering average scores in sub-subjects, participants in the blended program generally outperformed those in the traditional style. Conclusion: The blended teaching plan combines the convenience of online teaching with the benefits of offline practical instruction. The researchers contend that employing a blended teaching approach can enhance course efficiency, offering a viable alternative to the traditional teaching method.
CNN-Based Self-Attention Weight Extraction for Fall Event Prediction Using Balance Test Score

Injury, hospitalization, and even death are common consequences of falling for elderly people. Therefore, early and robust identification of people at risk of recurrent falling is crucial from a preventive point of view. This study aims to evaluate the effectiveness of an interpretable semi-supervised approach in identifying individuals at risk of falls by using the data provided by ankle-mounted IMU sensors. Our method benefits from the cause-effect link between a fall event and balance ability to pinpoint the moments with the highest fall probability. This framework also has the advantage of training on unlabeled data, and one can exploit its interpretation capacities to detect the target while only using patient metadata, especially those in relation to balance characteristics. This study shows that a visual-based self-attention model is able to infer the relationship between a fall event and loss of balance by attributing high values of weight to moments where the vertical acceleration component of the IMU sensors exceeds 5 m/s² during an especially short period. This semi-supervised approach uses interpretable features to highlight the moments of the recording that may explain the score of balance, thus revealing the moments with the highest risk of falling. Our model allows for the detection of 71% of the possible falling risk events in a window of 1 s (500 ms before and after the target) when compared with threshold-based approaches. This type of framework plays a paramount role in reducing the costs of annotation in the case of fall prevention when using wearable devices. Overall, this adaptive tool can provide valuable data to healthcare professionals, and it can assist them in enhancing fall prevention efforts on a larger scale with lower costs.
Multiple Watermarking Algorithms for Vector Geographic Data Based on Multiple Quantization Index Modulation

Multiple digital watermarking is an important and challenging task in geographic information science and data security. Vector geographic data are a basic data format for digital geographic data storage, and the security protection of these data involves copyright protection and tracking. As part of the solution, existing digital watermarking algorithms have made contributions to vector geographic data protection. However, when vector geographic data flow through multiple units, they need to be marked to ensure that the original data are not destroyed during data processing. Existing single or multiple data watermarking algorithms often fail in the presence of data processing because the new watermarks overlay the old ones. Consequently, a multiple digital watermarking algorithm based on multiple QIM (quantization index modulation) is proposed. First, based on traditional quantization index modulation (QIM), a multiple QIM is proposed. Unlike traditional QIM, in multiple QIM, the process of quantization is executed multiple times depending on the number of watermarks. Then, the vertices are quantized into different quantization intervals according to the multiple QIM. Finally, multiple watermarks are embedded into different quantization intervals to reduce the interference among multiple watermarks, and the original watermarks are not needed in the process of watermark detection. We then conducted experiments to test the multiple watermark method’s robustness and capacity, with an emphasis on datasets with a lower data volume. The experimental results show that the proposed algorithm achieves good performance in terms of its robustness against common issues, such as vertices addition, data simplification, data cropping, and feature deletion; this holds true for both normal and small amounts of data. Additionally, it has a high multiple watermark capacity.
A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT

The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary t members, ensuring the key’s security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection.
Transformer-Based Composite Language Models for Text Evaluation and Classification

Parallel natural language processing systems were previously successfully tested on the tasks of part-of-speech tagging and authorship attribution through mini-language modeling, for which they achieved significantly better results than independent methods in the cases of seven European languages. The aim of this paper is to present the advantages of using composite language models in the processing and evaluation of texts written in arbitrary highly inflective and morphology-rich natural language, particularly Serbian. A perplexity-based dataset, the main asset for the methodology assessment, was created using a series of generative pre-trained transformers trained on different representations of the Serbian language corpus and a set of sentences classified into three groups (expert translations, corrupted translations, and machine translations). The paper describes a comparative analysis of calculated perplexities in order to measure the classification capability of different models on two binary classification tasks. In the course of the experiment, we tested three standalone language models (baseline) and two composite language models (which are based on perplexities outputted by all three standalone models). The presented results single out a complex stacked classifier using a multitude of features extracted from perplexity vectors as the optimal architecture of composite language models for both tasks.
Unveiling the Threat: Investigating Distributed and Centralized Backdoor Attacks in Federated Graph Neural Networks

Graph Neural Networks (GNNs) have gained significant popularity as powerful deep learning methods for processing graph data. However, centralized GNNs face challenges in data-sensitive scenarios due to privacy concerns and regulatory restrictions. Federated learning (FL) has emerged as a promising technology that enables collaborative training of a shared global model while preserving privacy. While FL has been applied to train GNNs, no research focuses on the robustness of Federated GNNs against backdoor attacks. This paper bridges this research gap by investigating two types of backdoor attacks in Federated GNNs: centralized backdoor attacks (CBA) and distributed backdoor attacks (DBA). Through extensive experiments, we demonstrate that DBA exhibits a higher success rate than CBA across various scenarios. To further explore the characteristics of these backdoor attacks in Federated GNNs, we evaluate their performance under different scenarios, including varying numbers of clients, trigger sizes, poisoning intensities, and trigger densities. Additionally, we explore the resilience of DBA and CBA against two defense mechanisms. Our findings reveal that both defenses can not eliminate DBA and CBA without affecting the original task. This highlights the necessity of developing tailored defenses to mitigate the novel threat of backdoor attacks in Federated GNNs.
CrossTLNet: A Multitask-Learning-Empowered Neural Network with Temporal Convolutional Network–Long Short-Term Memory for Automatic Modulation Classification

Amidst the evolving landscape of non-cooperative communication, automatic modulation classification (AMC) stands as an essential pillar, enabling adaptive and reliable signal processing. Due to the advancement of deep learning (DL) technology, neural networks have found application in AMC. However, the previous DL models face the inter-class confusion problem in high-order modulations. To address this issue, we propose a multitask-learning-empowered hybrid neural network, named CrossTLNet. Specifically, after the signal enters the model, it is first transformed into two task components: in-phase/quadrature (I/Q) form and amplitude/phase (A/P) form. For each task, we design a method that combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network to effectively capture long-term dependency features in high-order modulations. To enable interaction between these two different dimensional features, we innovatively introduce a cross-attention method, thereby further enhancing the model’s ability to distinguish signal features. Moreover, we also design a simple and efficient knowledge distillation method to reduce the size of CrossTLNet, making it easier to deploy in real-time or resource-limited scenarios. The experimental results indicate that the suggested method exhibits exceptional performance in AMC on public benchmarks, especially in high-order modulations.
Joint and Individual Component Regression

Multi-group data, which include the same set of variables on separate groups of samples, are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. Our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. We formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores. We us the Continuum Regression (CR) to estimate the latent scores, which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. We conduct simulation studies and analysis of an Alzheimer's disease dataset to further demonstrate the effectiveness of JICO. R package of JICO is available online at https://CRAN.R-project.org/package=JICO. Supplementary materials for this article are available online.
Conclusions from the Analysis of Blockchain Solutions

The development of blockchain technology and distributed ledgers indeed contributes to enabling the delivery of various digital services, such as financial services, registry management, and tokens, including non-fungible tokens (NFTs). In blockchain systems, decisions are made based on the so-called consensus mechanism, which is a method of selecting a single version of transaction history that all nodes consistently agree upon. Although this technology is classified as emerging, it has a history of over ten years, and its ongoing implementations, including in the public administration sector, demonstrate its evolution and its status as one of the most promising technologies in terms of ensuring data immutability. The published ISO (International Organization for Standardization) standards and numerous implementations using business models executed in the DAO (Decentralized Autonomous Organization) architecture undoubtedly support the development of blockchain.
Future competencies and personnel training for customs authorities: Regional aspect

The educational subsystem is recognized as the most important driving force of regional socio-economic development. The policy of the Government of the Tyumen Region is aimed at promoting the development of international relations, expanding foreign economic relations, and popularizing export activities. For the regional economy development through the foreign economic activity sphere, we need personnel who meet the needs of regional employers, meet modern requirements for the specialist training quality, and have the competencies of the future. A special place in the foreign economic activity infrastructure is assigned to customs authorities. It is noted that modern strategic trends in the development of the customs service of Russia require the modernization of the educational process of training personnel for this area. The issues of a competence model formation are considered on the example of training a customs specialist at Tyumen State University, in which the emphasis is shifted to new groups of competencies, relationship of individual competencies, and synergetic effect of their interaction.
Static Mixer Apparatus for blending Ozone with water in the Process Pipeline

The article describes the procedures and results of directly mixing ozone with treated water in the process pipeline. The issue of disinfecting drinking water is significant because water is a vital resource for economic activity. Modern researchers are developing innovative disinfection technologies, such as mixing ozone-air with treated water in the process pipeline. The formation and quality of water in a reservoir were investigated, as well as the effect of ozone on the physicochemical parameters of water taken from the reservoir. The study presented a prototype disinfectant system and confirmed the findings of other researchers that swirling water flow is the most efficient way to mix 99% of ozone released into the pipeline. The study determined a treatment mode that provides the maximum disinfection efficiency while using the least amount of ozone.
Multiplexing Quantum and Classical Channels of a Quantum Key Distribution (QKD) System by Using the Attenuation Method

The primary goal in this paper is to verify the possibility of combining a quantum channel into a single optical fiber with other classical channels by using the so-called attenuation method. Since the quantum channel is very weak in terms of power, combining it into a single fiber with much more powerful classical channels is challenging. Thus, sufficiently high-quality filtering is important to avoid possible crosstalk. A second and more difficult problem to address is the interference caused by Raman noise, which increases with the fiber length and is also dependent on the input power of the classical channel. Thus, in this paper the focus is on the possibility of suppressing the Raman noise effect, both in advance by means of wavelength positioning and by means of installed optical components. Such phenomena must be considered in the route design, as the quantum channel must be placed at a suitable wavelength with respect to the classical channels. The influence of other nonlinear phenomena has been neglected. In this paper, a practical experiment aimed at building a fully functional multiplexed quantum key distribution link is also described.
Some areas of development of foreign cooperation in the higher education system

In this paper presents the essence of international cooperation in the higher education system, the content, opportunities, some directions and aspects of developing effective relations based on this joint cooperation relationship, and the methods of its implementation are sufficiently described on the example of the system of higher education institutions in our country. Also, research idea and implemented results aimed at interdependently harmonizing, integrating, coordinating and improving foreign cooperation relations between high education organizations of the two countries in the higher education system are highlighted. Also, the conclusions aimed at the development of educational-cooperative relations of students in foreign countries in the higher education system are stated, the results of the questionnaire survey conducted among students are highlighted, and the usability aspects of cooperative relations are proposed based on these results.
Ways to Develop Financial Resources in the Innovative Development of the Economy

This article examines strategies for amassing financial resources to spur innovative economic development, a pressing global issue, particularly in light of the recent pandemic. Emphasis is placed on maintaining operational continuity in production and services by funnelling funds from national and international capital markets into the corporate sphere, preserving the financial stability of joint-stock companies, ensuring employment, and introducing novel financing mechanisms. The paper also provides a detailed analysis of economic studies on the structure and characteristics of capital markets. The process of garnering financial resources from capital markets, guided by cash flow direction and the deployment of hybrid financial instruments during finance agreement negotiations, is also discussed. Overall, the article seeks to elucidate and address the challenges of driving innovation in economic growth through effective financial resource management.
A Phenomenological Approach to Wearable Technologies and Viscerality: From embodied interaction to biophysical music performance

During the past decade, embodied knowledge has provided novel important insights to rethink mediation technology, thereby paving the way for a transdisciplinary approach to wearable technologies. Stemming from a phenomenological-based approach and considering current trends in sonic interaction design, this article proposes an extensive account on embodied approaches to mediation technology and underlines the increasing importance of somatic knowledge within the field. It also presents an autoethnographic analysis of my own performance, which provides an original contribution to the artistic application of wearable technologies. Stemming from an ongoing research-creation on musical improvisation with biophysical technologies, the case study emphasises how an embodied and visceral approach to interaction can transform wearable devices into an active sensory-perceptual mode of experiencing, which is capable of stimulating the performer’s sensorimotor metaplasticity. The reconfiguration of a body’s automations through the use of sound feedback is a process that unfolds with a high degree of sensitivity in which the body can be poetically understood as an emergent territoriality, inhabited and transfigured by the sound.
MOTIVASI MAHASISWA MELANJUTKAN PENDIDIKAN KE IKIP MUHAMMADIYAH MAUMERE PROGRAM STUDI PENDIDIKAN EKONOMI ANGKATAN 2022

Student Motivation to Continue Education at IKIP Muhammadiyah Maumere Economic Education Study Program Class of 2022. Paper. Maumere: Faculty of Education of Social Sciences and Humanities Maumere Muhammadiyah Institute, 2023. This study aims to determine the motivation among students of the IKIP Muhammadiyah Maumere Economic Education Study Program Class of 2022 in continuing their education. The subjects of this research were 10 students from batch 2022 of the Economics Education Study Program, IKIP Muhammadiyah Maumere. This research use desciptive qualitative approach. The instruments used in this study were observation, interviews and documentation. The results of this study show that there are two motivations for students who continue their education at the IKIP Muhammadiyah Maumere campus for the economic education study program class of 2022, in general there are two, namely motivation that is in accordance with ideals (goal direction), and motivation that is not in accordance with ideals. Differences in the motivation of students who continue their education at the IKIP Muhammadiyah Maumere program for economic education studies class of 2022 such as no initial motivation, different orientation, or other needs apart from the initial motivation gave birth to four different levels of needs, namely self-actualization, appreciation, social needs, and feeling safe.