Reviewerly/exHarmony huggingface dataset:
REGARDING THE DEVELOPMENT OF EDUCATIONAL AND SCIENTIFIC POTENTIAL IN THE 21ST CENTURY:

Modern digital technologies contribute to the transformation of the social order and the integration of the latest information processing capabilities into everyday life. These trends are also characteristic of education and science as separate branches of the social whole. Therefore, the aim of the work is to analyze the technological challenges in the development of the educational and scientific potential of the XXI century. The methods of analysis and synthesis, comparativistic method, forecasting, modeling method were used in the study. The main empirical method was a survey in which 130 teachers and 100 scientific workers participated. It was found that the majority of teachers consider the use of digitalization achievements as inevitable and positive phenomena. Researchers are less optimistic - almost every third employee of the industry did not give a positive response to the impact of digitalization. It is determined that such a phenomenon is negatively perceived because of its ambiguous impact on academic virtue. Modern academics and educators equally understand the importance and significance of digital competence in today's globalized and informatized world. Among the problems, respondents named the technical equipment of educational and research institutions as relevant. Especially for scientists was the problem of insufficient funding. The conclusions noted the importance of conducting further research into the security of using digital tools in research and educational activities and defining a philosophical basis for the transformations, which will help to predict possible challenges in the future.
Extended Kalman Filter State Estimation for Autonomous Competition Robots

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition robot. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.
Developing Human Skills in the Era of Artificial Intelligence: Challenges and Opportunities for Education and Training

The rapid advancement and adoption of artificial intelligence (AI) presents significant challenges and opportunities for human skills in the 21st century. AI systems can do many things that people usually do, like looking at data, making decisions, and understanding language. However, AI also creates new demands for human skills that complement and enhance the capabilities of AI, such as creativity, critical thinking, communication, and collaboration. This paper conducts a comprehensive review of the literature pertaining to the impact of artificial intelligence on human abilities and its implications for education and training. It discusses the key human-centric skills that are important for working with and alongside AI and the current and future challenges of developing and assessing these skills. It also looks at how new pedagogical approaches and technologies can help humans and AI work together and learn. The paper concludes with some recommendations for policy makers, educators, and learners to foster human-centric skills in the era of AI.
Two-party quantum identity authentication without entanglement

This study proposes a two-party quantum identity authentication protocol that does not require the participation of a third party. The protocol can verify the user’s identity without exposing the authentication key information, and it can be implemented with a single-photon state, which does not involve entanglement, enabling it to be combined with existing technologies. The shared key is protected by the protocol and hidden during communication, and the security of the authentication scheme is analyzed and verified under general attack. Further analysis results indicate that this protocol is secure and efficient.
Harnessing Knowledge-Based Concepts for Streamlined Mind Mapping in Academic Study

The main goal of this research project is to build and implement a knowledge-based system that will produce more accurate and significant mind maps from textual input. This study suggests the creation of a knowledge-based system devoted to mind map construction, as opposed to current text mining algorithms and machine learning techniques, which might not be able to properly capture the semantic subtleties of input material. The suggested system aims to reduce the constraints of conventional techniques by incorporating domain-specific information and rules into the analytical process. The accuracy and relevance of the produced mind maps are projected to improve with the addition of this knowledge. This project's main objective is to build a knowledge-based framework that overcomes the limitations of the existing text mining and machine learning approaches, resulting in more sophisticated and theoretically rich mind maps from textual data.
Secondary employment of modern students (on the example of AltSU students)

Currently, a student who simultaneously receives a higher education and works somewhere is becoming quite commonplace. Moreover, the tendency to combine study and work is characteristic of the whole world. Secondary employment allows students not only to improve their financial situation, become financially independent from their parents, but also to acquire a number of communicative and professional competencies that can be successfully applied in the future. Moreover, the practice of secondary employment allows you to form a certain work experience, which can be an advantage in employment after graduation. The phenomenon of secondary employment of a student is difficult to assess unambiguously, on the one hand, there is an accelerated professional socialization of an individual, his rapid inclusion in a new social reality, into which a young person will have to move fully after graduation. The student becomes more responsible, manages his time more competently. On the other hand, secondary employment is realized precisely in their free time from study, negatively affecting the amount of time allocated to prepare for classes, sessions, etc. The key problem of secondary employment of a student is that often the specialty of study is in no way connected with the place of work. This fact increases the likelihood that an individual after graduating from university will go to work not in his specialty, which makes it more difficult for the demands of the labor market and the education system to produce necessary quantity of specialists a certain domain. Also, not all students are able to correctly prioritize work and academic activities, as a result of which there are distortions that negatively affect educational activities.
High-quality parallel entangling gates in long mixed-species ion chains

A universal quantum computer is an ultimate pursuit, which should possess the ability to perform entangling gate operations in parallel with high quality in a scalable fashion. However, for a long trapped-ion chain, the simultaneous implementation of two-qubit quantum gates is a challenging task due to the drastic increase of the crosstalk resulting from the collective motional modes. In this paper, by utilizing the structure of the localized motional modes in the long mixed-species ion chain, we propose a highly efficient linear iterative scheme to eliminate the crosstalk within the neighboring pairs. We show that this scheme allows the realization of high-fidelity, low-crosstalk, scalable, parallel two-qubit gates with only a few pulse segments for the long chain. In addition, by considering the practical experimental feasibility, we optimize the laser power and improve the robustness against the random static drifts of the motional frequencies.
Proyeksi Kebutuhan Ruang Kelas SD Kabupaten Subang Berdasarkan Penduduk Usia Sekolah 2021-2025

This study examines the problem of the classroom needs analysis based on projections of elementary school age-population growth. Research aims to: a) get information on the projection of the total population (SAP, PCN, and total number of students based on NER); b) get information on the projected number of new students absorbed by elementary school; and c) get projected information on the number of classrooms needed. The method used in this research is descriptive method with qualitative approach. In this research, the data sources were the elementary school Facilities and Infrastructure Staff and Head of the Department of Population and Civil Registration. Data collected by the study of documentation, interviews, and observations. The results of the research conducted indicate that: a) the population is increasing every year; b) SAP and PCN have decreased every year; c) NER achievement, generally decreased from 2016-2019 and increased in 2020; d) ASK is experiencing an increasing trend so that the number of new students each year has increased; e) the number of classrooms in 2021 which is calculated based on 1000 residents requires 611 elementary schools, based on national education standards still less 39 units
A Multi-Task Learning Speech Synthesis Optimization Method Based on CWT: A Case Study of Tacotron2

Abstract Text-to-speech (TTS) synthesis plays an essential role in human-computer interaction. Currently, the vast majority of TTS acoustic models select only the Mel spectrum as an intermediate feature from text to speech. However, the resulting Mel spectrograms can be ambiguous in some aspects due to the poor ability of the Fourier transform to capture the mutation signal during the acquisition of the Mel spectrograms. With the aim of improving the clarity of synthesized speech, we propose a multi-task learning optimization method and conduct experiments on Tacotron2 speech synthesis system to demonstrate the effectiveness of the proposed method. Our method introduces an additional task: wavelet spectrograms. The continuous wavelet transform (CWT) has been widely used in tasks such as speech enhancement and speech recognition due to its ability to adaptively vary the time-frequency resolution and its excellent performance in capturing non-stationary signals. This study demonstrates, both theoretically and experimentally, that the clarity of Tacotron2 synthesized speech can be improved by introducing Wavelet-spectrogram as an auxiliary task: a CWT feature extraction network is added, and Wavelet-spectrogram features are extracted from the Mel spectrum output by the decoder. Experimental results show that the Mean Opinion Score (MOS) achieved for the speech synthesized by the model using multi-task learning are 0.17 higher than with the baseline model. Furthermore, based on the analysis of the reasons for the success of the CWT based multi-task learning method in the Tacotron2 model and the effectiveness of multi-task learning, we conjecture that the proposed method can be applied to improve the performance of other acoustic models. This approach represents a promising direction for future research.
Rancang Bangun Sistem Manajemen Koleksi Elektronik Perpustakaan Berbasis Web

The rapid development of science and technology has given rise to what is known as the digital era, an era marked by the birth of a generation of digital natives. The birth of this era demands adaptive changes for every organization or institution including libraries in providing services to this 'new' generation. The library itself is currently inseparable and must be in line with the development of existing information technology so that it can carry out its role and function more optimally in providing the best service to users who are the digital generation. The Ciputra Surabaya University Library is a form of higher education library that is expected to be able to meet the needs of the teaching and learning process for its academic community and also carry out its functions in preserving culture and archives, as well as developing knowledge for the general public. Good collection management will make the library the main place for users to meet the desired information needs, this is because good collection management will make the collections contained in a library exactly according to the needs of users and of course this is in line with the increasing collections owned by the library. itself. Therefore a collection management strategy is needed by the Ciputra University Library in Surabaya. Based on the results of observations made by the author, it was found that there is an opportunity to develop a web-based library and museum collection management system design, making it easier and faster for users to access collection information at the Ciputra University Library in Surabaya.
Using Gamification in the English Classroom: Impact on Motivation and Learning Outcomes

This study aims to explore the use of gamification in English classes and analyze its impact on student's motivation and learning outcomes. The research method used in this article is qualitative with a library research approach by analyzing data sources from various literatures. Data collection through scientific journals, books, research reports, and other related documents relevant to this research topic. Data analysis by juxtaposing theories and previous research findings. The study results show that gamification benefits students' academic performance in English classes and dramatically increases their motivation and enthusiasm for learning so that the learning outcomes are satisfactory. This research provides a new contribution to understanding how gamification can be applied in an educational context and valuable recommendations for educators and decision-makers to improve the quality of teaching English in educational environments.
Sistem Inventory Menggunakan Metode Supply Chain Management dalam Mengatur Jumlah Ketersediaan Obat pada Apotik

The development of information technology provides great benefits and convenience, both in terms of data processing and presentation of information on inventory of existing goods in organizations, agencies or companies. One of the business processes that can be developed through information technology is the company's supply chain management process. An inventory information system using a good Supply Chain Management (SCM) method will increase the productivity and performance of the company. In conducting the research conducted at the pharmacy, the aim was to improve the old system. By conducting direct observations in the field, several weaknesses were found from the system that was running at the Arafah Padang Pharmacy. The results of the analysis are applied in an application that is able to process inventory system data using the Supply Chain Management (SCM) method. With the design of an inventory system with the Supply Chain Management (SCM) method, it can be more helpful in processing drug inventory data and the existing inventory system can increase the effectiveness and work efficiency of Pharmacy employees.
Suppressing Error Floors in SCPPM via an Efficient CRC-aided List Viterbi Decoding Algorithm

List Viterbi decoders are a very effective way to improve the performance of block codes in combination with an error detection outer code. In this work, we combine an efficient serial list Viterbi decoder design with an existing serially-concatenated, convolutionally-encoded, pulse position modulated code (SCPPM) used in space communication, that exhibits poor performance because of an error floor. The SCPPM code features a 32-bit CRC that provides powerful error detection capability and an outer four-state convolutional code that makes it suitable for a list Viterbi decoder. The system’s code is very long, consisting of 15, 120 bits, which renders a high complexity decoder impractical, while the high error detection allows for a list decoder with very low undetected error probability. We use a very efficient list Viterbi decoder algorithm to avoid most of the redundant operations to produce low complexity serial list Viterbi decoder. The combined system reduces the error floor, moderately for the original version of the system, and completely suppresses it when the code length is increased to four times longer.
Expand Object Detection Dataset Automatically via Deep Learning Neural Networks

Object detection is an important research field in image processing. The state-of-the-art machine learning models are trained with huge number of samples to solve this problem. Manually labeling objects on images is labor intensive, expensive, and time consuming. It is desirable to create an automatic labeling system to reduce the cost and the time needed. A deep learning neural networks based automatic labeling system is proposed in this paper to expand Microsoft Common Objects in Context 2017 (COCO 2017) dataset from 80 classes to 81 classes by adding labels for the new class, human faces, automatically. The system contains an Auto-Labeling Model (ALM) which is trained using Web Image Dataset for Event Recognition FACE (WIDER FACE) dataset. ALM detects faces in COCO 2017 dataset and provides bounding boxes automatically with very high accuracy. The generated new labels by ALM are merged to the existing labels in COCO 2017 dataset automatically. 4,110 hours human effort is saved using this system. The expanded COCO 2017 dataset is evaluated quantitatively and visually to ensure the efficiency of the proposed auto-labeling system.
Communication-Efficient Personalized Federated Learning with Privacy-Preserving

Federated learning (FL) gets a sound momentum of growth, which is widely applied to train model in the distributed scenario. However, huge communication cost, poor performance under heterogeneous datasets and models, and emerging privacy leakage are major problems of FL. In this paper, we propose a communication-efficient personalized FL scheme with privacy-preserving. Firstly, we develop a personalized FL with feature fusion-based mutual-learning, which can achieve communication-efficient and personalized learning by training the shared model, private model and fusion model reciprocally on the client. Specifically, only the shared model is shared with global model to reduce communication cost, the private model can be personalized, and the fusion model can fuse the local and global knowledge adaptively in different stages. Secondly, to further reduce the communication cost and enhance the privacy of gradients, we design a privacy-preserving method with gradient compression. In this method, we construct a chaotic encrypted cyclic measurement matrix, which can achieve well privacy protection and lightweight compression. Moreover, we present a sparsity-based adaptive iterative hard threshold algorithm to improve the flexibility and reconstruction performance. Finally, we perform extensive experiments on different datasets and models, and the results show that our scheme achieves more competitive results than other benchmarks on model performance and privacy.
Energy Management, Control, and Operations in Smart Grids: Leveraging Blockchain Technology for Enhanced Solutions

As smart grids advance rapidly, they are evolving along two primary trajectories: (1) digitalization through the incorporation of Internet of Things (IoT) technology and intelligent control, and (2) decentralization by leveraging small-scale distributed energy sources for control. However, these developments also introduce complexities in the functioning, management, and control of smart grids. Consequently, there is an urgent need for a transparent, secure, robust, transactive, and scalable framework for all stakeholders and operators. Blockchain technology emerges as a promising solution for this new smart grid paradigm, providing a range of features, including a distributed ledger, immutability, consensus, security, and automation. This article highlights the challenges in smart grid management, control, and operations, and discusses the potential of blockchain-based solutions to address these issues. It demonstrates that the adoption of blockchain can significantly enhance the overall functioning of the infrastructure by establishing a decentralized architecture without central governance.
Infusing Blockchain in accounting curricula and practice: expectations, challenges, and strategies

Blockchain, or distributed ledger technology, is acknowledged as the most significant and disruptive innovation in accounting since the double-entry system. All the ‘Big Four’ accounting firms and several major S&amp;P500 companies have invested considerable resources in developing blockchain technologies. Some maximalists of this technology have even hinted that it will fundamentally change accounting and auditing if all transactions can be captured in an immutable blockchain. It is a daunting task for accounting academics to determine how to infuse blockchain in accounting curricula since the body of knowledge in this area spans several disciplines, such as, accounting, economics, finance, computer science, and engineering. It is also difficult for accounting practitioners to know what aspects of this technology are relevant to accountants for the same reason. In this paper, using the diffusion of innovation theory, I help explain why we need to incorporate the accounting-relevant aspects of blockchain in accounting curricula and practice and how we can accomplish that goal without introducing unnecessary technological complexity and jargon. I also provide eight case studies, which were successfully trialled by me at CPA organization/association conferences, that can be used to communicate the accounting relevant aspects of blockchain in the domains of accounting, tax, and audit services.
Sentiment Analysis of Customer Reviews Using Support Vector Machine and Smote-Tomek Links For Identify Customer Satisfaction

Shopping activities in the online market, especially fashion trends, continue to increase with all the promo efforts offered. One of the considerations for buying products on the online market is to read reviews. Each consumer review shows the level of interest in the product. The number of negative reviews and the emergence of many varied reviews pose a problem in categorizing reviews. Sentiment analysis is a way of looking at the polarity of reviews to classify positive and negative reviews. The Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links are applied in this study. Classification using the Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links showed better results with an Accuracy of 0.92, Precision of 0.89, Recall of 0.89, and F1-score of 0.89 than without the combination of the Synthetic Minority oversampling Technique (SMOTE) with Tomek Links with an Accuracy of 0.68, Precision of 0.55, Recall of 0.99, and an F1-score of 0.71.