Reviewerly/exHarmony huggingface dataset:
Students’ engagement in research at the university: A sociological analysis

One of the priorities of Russia’s scientific-educational policy is to increase the attractiveness of a career in science and higher education. The Russian government implements a number of federal programs aimed at involving the youth in research activities. Students’ research work is a traditional integral part of the educational process, an element of the generational continuity, of the formation and renewal of scientific schools, of tutoring and professional training. However, when students are involved in research, there are difficulties determined by the peculiarities of the organization of the higher education and by the students’ personal characteristics. The article considers the features of the participation of the Russian universities’ students in research, their scientific achievements and problems that limit or hinder their involvement in research activities, reduce their motivation and satisfaction with studies in general. The article is based on the survey of the Russian universities’ students, conducted by the Center for Education Development Strategy of the Moscow State University in November - December 2022 (N = 123977). The results of the survey show that, when studying at the university, students differ significantly in the quality and forms of training, realization of individual abilities and inclinations, level of motivation and requests for higher education. Therefore, the educational process should be organized in such a way as to comprehensively realize the students’ individual abilities, provide a flexible approach to educational trajectories and support the involvement of the majority of students in research activities.
Scalable Binary Neural Network applications in Oblivious Inference

Binary neural network (BNN) delivers increased compute intensity and reduces memory/data requirements for computation. Scalable BNN enables inference in a limited time due to different constraints. This paper explores the application of Scalable BNN in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on his/her data by a trained model held by the server without disclosing the data or learning the model parameters. Two contributions of this paper are: 1) we devise lightweight cryptographic protocols explicitly designed to exploit the unique characteristics of BNNs. 2) we present an advanced dynamic exploration of the runtime-accuracy tradeoff of scalable BNNs in a single-shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under various computational budgets. Compared to CryptFlow2, the state-of-the-art technique in the oblivious inference of non-binary DNNs, our approach reaches 3 × faster inference while keeping the same accuracy. Compared to XONN, the state-of-the-art technique in the oblivious inference of binary networks, we achieve 2 × to 12 × faster inference while obtaining higher accuracy.
A Study on Changes in Cognitive Attitudes toward the Climate Crisis of International Students for Academic Purpose - Focused on Media Literacy Education -

This study aims to analyze the impact of media literacy education on economic and social problems caused by climate change on the cognitive attitudes of international students pursuing academic goals. The findings reveal that while the learners were aware of the seriousness of the climate crisis, they lacked understanding of its links with unemployment, fossil fuel use, and meat consumption. In addition, there was a significant difference in perception of unemployment and climate problems depending on exposure to climate education. Media literacy education activities facilitated a shift in perception among learners, demonstrating that fossil fuels are the primary contributor to the climate crisis and cause substantial pollution during the processing, production, and sale of meat. The study highlights the need for education related to the climate crisis, particularly for international students pursuing academic goals, to develop specific action plans in order to address the issue.
Transformer in mRNA Degradation Prediction

The unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performance metric. The Nucleic Transformer has been proposed by different researchers as a deep learning solution that is able to utilize a self-attention mechanism and Convolutional Neural Network (CNN). Hence, this paper would like to enhance the existing Nucleic Transformer performance by utilizing the AdaBelief or RangerAdaBelief optimizer with a proposed decoder that consists of a normalization layer between two linear layers. Based on the experimental result, the performance of the enhanced Nucleic Transformer outperforms the existing solution. In this study, the AdaBelief optimizer performs better than the RangerAdaBelief optimizer, even though it possesses Rangerâ€™s advantages. The advantages of the proposed decoder can only be shown when there is limited data. When the data is sufficient, the performance might be similar but still better than the linear decoder if and only if the AdaBelief optimizer is used. As a result, the combination of the AdaBelief optimizer with the proposed decoder performs the best with 2.79% and 1.38% performance boost in public and private MCRMSE, respectively.
Factors Influencing Accounting Students' Interest in Continuing Masters of Accounting Education (Study on Students of the Accounting Study Program, Faculty of Economics and Business, Universitas Jambi)

This study aims to determine the effect of motivation, educational costs, length of education, and peer environment on the interest of accounting students to continue their Masters in Accounting. The sample of this study was 107 Accounting students at the Faculty of Economics and Business, Jambi University, using a probability sampling technique. Data collection was carried out by distributing questionnaires. The data analysis method used is Partial Least Square (PLS) using SmartPLS 3.0 software. The results showed that motivation, educational costs, and peer environment had a significant positive effect on students' interest in continuing their Masters in Accounting Education. Meanwhile, the length of education has no effect on students' interest in continuing their education with a master's degree in accounting.
Early Detection for Refrigerant Leakage based on Radar System Operation Data

정비전략은 고장정비와 예방정비를 넘어서, 장비의 상태를 모니터링하고 센싱데이터를 분석하여 필요한 시점에 필요한 정비만을 수행하는 상태기반정비(Condition-based maintenece, 이하 CBM)로 발전 중이다. CBM의 중요성이 강조됨에 따라 국방 분야에서도 CBM도입을 위한 제도적 개선 및 관련 연구들이 수행되고 있다. 그러나 기존 CBM 관련 연구는 실 운용데이터 획득의 어려움으로 인해 주로 실험실 환경에서의 이루어지고 있어, 실 운용환경에 대한 실증적 연구가 부족하다. 본 논문에서는 전력화되어 운용중인 레이더 체계의 운용 데이터를 활용한 냉매 누설 사전 감지 연구사례를 제시한다. 냉매 누설 시 송신 신호 증폭을 담당하는 고발열 부품들에 대한 적절한 냉각이 이루어지지 않아 체계성능 및 신뢰성 저하를 유발할 수 있으나, 이를 사전에 식별할 수 있는 진단 방법이 수립되어 있지 않다. 냉매 누설 사전감지를 위하여 본 연구에서는 대상 레이더 체계의 냉각장치에 기 설치된 센서로부터 수집되는 데이터 중 냉매 누설과 연관된 데이터 항목을 도메인 지식에 기반하여 선별하였다. 고장이 발생하지 않은 운용초기 데이터로 LSTM(Long Short-Term Memory)-Autoencoder를 학습하였다. 운용초기 이후 정상과 이상이 섞여있는 데이터를 입력하여 냉매누설 감지 성능을 검증한 결과, 실제 운용 및 정비 간 고장을 탐지한 시점보다 앞서 이상을 감지할 수 있음을 확인하였다. The maintenance strategy is changing from corrective and preventive maintenance to condition-based maintenance (CBM). CBM is a proactive repair activity based on the health status of a system at any particular time. As the importance of CBM is emphasized, institutional improvement and research are being made for CBM in the defence area. On the other hand, existing research has been conducted in laboratory-scale experiments, so an empirical study on CBM for field environments is insufficient. This paper discusses refrigerant leakage detection of radar systems based on field operation data. Refrigerant leakage leads to insufficient cooling for highly heated components that amplify the transmission signal, but there is no method to detect refrigerant leakage. In this research, the data related to refrigerant leakage were extracted based on domain knowledge. LSTM (Long Short-Term Memory)-Autoencoder was trained on normal operation measurement in the early operation period. The data for the remaining period that comprised both normal and anomaly conditions of the cooling system were input into the trained LSTM-Autoencoder model. The result showed that anomaly data were detected prior to real failure.
Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Detecting foreground objects in video is crucial in various machine vision applications and computerized video surveillance technologies. Object tracking and detection are essential in object identification, surveillance, and navigation approaches. Object detection is the technique of differentiating between background and foreground features in a photograph. Recent improvements in vision systems, including distributed smart cameras, have inspired researchers to develop enhanced machine vision applications for embedded systems. The efficiency of featured object detection algorithms declines as dynamic video data increases as contrasted to conventional object detection methods. Moving subjects that are blurred, fast-moving objects, backdrop occlusion, or dynamic background shifts within the foreground area of a video frame can all cause problems. These challenges result in insufficient prominence detection. This work develops a deep-learning model to overcome this issue. For object detection, a novel method utilizing YOLOv3 and MobileNet was built. First, rather than picking predefined feature maps in the conventional YOLOv3 architecture, the technique for determining feature maps in the MobileNet is optimized based on examining the receptive fields. This work focuses on three primary processes: object detection, recognition, and classification, to classify moving objects before shared features. Compared to existing algorithms, experimental findings on public datasets and our dataset reveal that the suggested approach achieves 99% correct classification accuracy for urban settings with moving objects. Experiments reveal that the suggested model beats existing cutting-edge models by speed and computation.
Enriching Image Archives via Facial Recognition

The digitization of image archives across the globe has opened up vast collections of libraries, museums, and cultural heritage institutions. These collections provide valuable historical information to the public and researchers. Many image collections have little metadata describing who or what is depicted in a structured format, making it difficult to search for specific persons. This work presents a facial recognition pipeline to enrich these collections by recognizing the persons in each image. A reference dataset of over 6,000 known persons was constructed and facial recognition was performed on a dataset of over 150 thousand images. Detected faces were matched with the known faces using a similarity score on the face embeddings. We developed an interactive labeling tool to efficiently validate the face recognition predictions. A total of 182 thousand detected faces were labeled with this tool. Using a minimum similarity score of 0.5, the face recognition model achieved a precision of 0.936 and identified over 62 thousand persons from the image archives. We show how clustering can be used to identify new persons that were not included in the reference dataset. Furthermore, we highlight the potential of facial recognition to enhance the accessibility of the collections and offer new insights.
Owner-free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an O wner-free Di stributed S ymmetric searchable encryption supporting C onjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) in dual-cloud architecture to generate search tokens with the data owner free from sharing key and being online. Then, we provide fine-grained conjunctive query in the distributed architecture using additive secret sharing and symmetric-key hidden vector encryption. Finally, formal security analysis and empirical performance evaluation demonstrate that ODiSC is adaptively simulation-secure and efficient.
Modulation and demodulation with quadrature square waves as carriers to improve spectrum utilization

Frequency division modulation enables fast and highly accurate detection of multiple signals, but its spectrum utilization is extremely low. Of course, existing methods improve the spectrum utilization by using quadrature signals as carriers, but it requires the carrier generation to be synchronized with the start of the acquisition. This is difficult to achieve when the carrier generation and the signal acquisition are not controlled by the same clock. Therefore, this paper proposes a demodulation method for the case where the carrier signal is a pair of quadrature square wave signals and the start of the carrier is not synchronized with the sampling. The method demodulates two signals modulating a pair of quadrature carriers by calculating the phase of one independent carrier signal. In this paper, three-wavelength multispectral images are acquired, and the demodulation results are compared with those acquired by the time-division method. The results show that there is a high similarity between the two, which proves the effectiveness of the method proposed in this paper, improves spectrum utilization, and is useful for the acquisition of multiplexed signals.
Application of Flipped Class Model in Teaching Elemental Compounds of Inorganic Chemistry-The Example of Teaching Practice on Carbon Group Elements

The flipped class model is applied to the teaching process of carbon group elements, which fully reflects the effective teaching of "student-oriented and teacher-led".The students complete the "learning first" according to their self-study task list before class.The class's QQ group, wechat group and other social softwares communicate and interact with teachers and students online, and the cloud class topic database is used to detect the feedback effect of learning first.In class, teachers collect the feedback information of students' "learning first", carefully design the teaching process of "fixed teaching" problem inquiry, and encourage students to participate actively.After class, teachers and students can further communicate and interact online.Students can conduct self-awareness, self-reflection, self-evaluation and self-regulation of the learned knowledge unit, complete knowledge construction and improve students' metacognitive ability.
The Deep Integration of High School Biology and STSE under the View Valve of Core Literacy

With the increasing emphasis on core literacy, the concept of STSE education has been widely used in high school biology courses. This paper analyzes the three levels of lesson preparation, teaching and evaluation, and finds that there is still much room for improvement in the implementation of STSE education, i.e., misunderstanding of the connotation of STSE education and homogenization of the selection of materials, lack of wholeness and participation in the teaching process, and "mechanization" or "randomization" of the educational evaluation. We have discussed how to select and optimize STSE teaching materials, strengthen the holistic and participatory nature of STSE teaching, and enrich the main body and content of evaluation.
Compliance with the secrecy regime during online training in educational organizations of the Ministry of Internal Affairs of Russia

Strategic priorities for the development of modern Russian society and ensuring law and order require the establishment of an effective continuous educational process in the universities of the Ministry of Internal Affairs of Russia. The author examines the issue of compliance with the secrecy regime in the process of online education of students in universities of the Ministry of Internal Affairs of Russia. The relevance of the study is due to the scale of the spread of coronavirus infection and the development of information and communication technologies. The universities of the Ministry of Internal Affairs of Russia have special disciplines that require secrecy in the process of online learning. These disciplines include operational investigative activities, operational investigative psychology, record keeping and secrecy regime, and others. The object of the study is the educational process organized online in universities of the Ministry of Internal Affairs of Russia. The subject of the study is the methods of transmitting and exchanging information in educational organizations of the Ministry of Internal Affairs of Russia within the framework of online distance learning. In the course of the study, the goal was achieved to substantiate the necessary and sufficient measures for the optimal implementation of educational programs of higher education, vocational training and advanced training of police officers in the conditions of expanding the information space, communication capabilities and limitations generated by the COVID 19 pandemic. The author conducted a comparative analysis of the relationship between the concepts of "distance learning (education)" and "online learning (education)". The conclusion is made about the need to expand the capabilities of the ISOD of the Ministry of Internal Affairs of Russia and the development of a single departmental digital educational portal of universities of the Ministry of Internal Affairs of Russia within this information space. The author suggests improving the information potential of the system of the Ministry of Internal Affairs of Russia for the possibility of teaching special disciplines in the process of education online.
Reinforcement Learning in Education: A Multi-armed Bandit Approach

Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment. Reinforcement leaning solves unsupervised problems where agents move through a state-action-reward loop to maximize the overall reward for the agent, which in turn optimizes the solving of a specific problem in a given environment. However, these algorithms are designed based on our understanding of actions that should be taken in a real-world environment to solve a specific problem. One such problem is the ability to identify, recommend and execute an action within a system where the users are the subject, such as in education. In recent years, the use of blended learning approaches integrating face-to-face learning with online learning in the education context, has increased. Additionally, online platforms used for education require the automation of certain functions such as the identification, recommendation or execution of actions that can benefit the user, in this sense, the student or learner. As promising as these scientific advances are, there is still a need to conduct research in a variety of different areas to ensure the successful deployment of these agents within education systems. Therefore, the aim of this study was to contextualise and simulate the cumulative reward within an environment for an intervention recommendation problem in the education context.
A Method to Improve the Accuracy of Personal Information Detection

It is necessary to confirm the personal data factors and the rules of verification before conducting personal data detection.So that the detection method can be written in the subsequent implementation of the automatic detection tool.This paper will conduct experiments on common personal data factor rules, including domestic personal identity numbers and credit card numbers with checksums.We use ChatGPT to test the accuracy of identifying personal information like ID card identification numbers or credit card numbers.And then use personal data correlation to reduce the time for personal data identification.Although the number of personal information factors found has decreased, it has had a better effect on the actual manual personal data identification.The result shows that it saves about 45% of the calculation time, and the execution efficiency of the accuracy is also improved with the original method by about 22%, which is about 2.2 times higher than the general method.Therefore, the method proposed in this paper can accurately and effectively find out the leftover personal information in the enterprise.
Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms

Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.
DEVELOPMENT OF A METHODOLOGY FOR TEACHING MATHEMATICS IN COLLEGES USING THE DYNAMIC PROGRAM «GEOGEBRA»

The article discusses the features of the organization of the educational process using the dynamic GeoGebra environment. A description is given of the methodological and technical stages of developing an interactive worksheet as an effective didactic tool. The content of the work presents examples of tasks from the "Similarity of Triangles" section in geometry and an algebraic task.