Reviewerly/exHarmony huggingface dataset:
Development of domestic education based on the use of innovative educational technologies in the context of new challenges

Aim. To consider the role of modern education in the context of using innovative educational technologies in the context of new challenges. Tasks. To determine the trends in the development of domestic education; to analyse the modern challenges that affect it; to consider the impact of digitalization of the educational process on the educational potential of students. Methods. This article traces the development of domestic education in various aspects with the help of general methods of scientific cognition. The tendencies characteristic of the current state are revealed, as well as the necessity and directions of further development of domestic education are determined. Results. The main trends in the development of domestic education in the Russian Federation (RF) and modern challenges that affect it are highlighted. Various training needs, questions about the choice of individual educational trajectory, specific pace of learning material mastering, emphasis on self-education, the need to comply with scientific and technological changes are analysed. The influence of digitalisation of the educational process on the educational potential of students is considered. Conclusions. The conclusion about the need for further transformation of education is formulated. In the conditions of new challenges it is necessary to introduce new innovative educational technologies in the educational process and to meet the modern demands of the market and society.
Vision-based Propeller Damage Inspection Using Machine Learning

Unmanned Aerial Vehicles (UAVs) play an increasingly pivotal role in day-to-day rescue operations, offering crucial aerial support in challenging terrain and emergencies, such as drowning. Drone hangars are strategically deployed to ensure swift response in remote locations, overcoming range-limiting constraints posed by battery capacity. However, the UAV's airworthiness, typically ensured through conventional inspections by a technical individual, is paramount to guarantee mission safety. Over time, UAVs are prone to degradation through contact with the external environment, with propellers often being the cause of flight instability and potential crashes. This paper presents an innovative approach to automate UAV propeller inspection to avert incidents preemptively. Leveraging visual recordings and deep learning methodologies, we train a Convolutional Neural Network (CNN) model using both passive and active learning strategies. Our approach successfully detects physical damage on propellers with an impressive accuracy of 85.8%, promising a significant improvement in maintaining UAV flight safety and effectiveness in rescue operations.
Smartphone Motion Sensor Data Processing for Driving Characteristics Classification

Driving behavior significantly influences road safety. Unsafe driving behaviors, such as driving under the influence, speeding, and using mobile phones, can lead to serious accidents and fatalities. This research aims to observe driving characteristics by utilizing smartphone motion sensor data. The data collection method involved recording the driver’s smartphone motion sensor during trips. The data were then exported from the system for further processing. The main objective of this study is to process the data by creating a classification model with the best performance in handling smartphone motion sensor data. The results of this research are expected to be implementable models to address road safety issues in the future. Additionally, by utilizing driver characteristic detection technology, awareness of safe driving practices can be enhanced. The research methodology used data mining with machine learning classification modeling using random forest (RF), support vector machine (SVM), and decision tree (DT) methods. The test results indicate that the RF model performed the best with an accuracy of 91.22%. Furthermore, this study found that speed was the most influential factor in identifying safe or unsafe driving behavior. The developed classification model shows the potential to improve traffic management efficiency and contribute to safer transportation. By leveraging driver characteristic detection technology, it is hoped that awareness of safe driving practices will increase, leading to a safer road environment.
The effect of personality of childcare teachers on the praise leadership and teaching metacognition

본 연구는 보육교사의 인성이 칭찬리더십과 교수메타인지에 미치는 영향력을 살펴보고 보육교사의 인성을 강화하는데 필요한 기초자료를 제공하는데 목적이 있다. 연구 참여자는 K시 지역의 어린이집에 근무하고 있는 보육교사 250명을 대상으로 설문조사하였다. 자료의 분석은 SPSS 26.0(ver.) 통계프로그램을 이용하여 기술통계와 다중회귀분석을 실시하였다. 결과를 살펴보면 첫째, 5점 리커트 척도를 이용할 때 보육교사의 인성 수준 3.88, 칭찬리더십 수준 3.89, 교수메타인지 수준 3.88으로 나타났다. 둘째, 보육교사의 인성 요인 중, 창의적 성향, 유머·위트, 의사소통, 주도적 자아성취, 이타심 순서로 칭찬리더십에 유의미한 영향을 미치는 것으로 나타났다. 셋째, 보육교사의 인성 요인 중, 직무수행 태도, 창의적 성향, 자기계발, 의사소통, 주도적 자아성취 순서로 교수메타인지에 유의미한 영향을 미치는 것으로 나타났다. 따라서 본 연구는 인성 요인의 일부가 칭찬리더십과 교수메타인지에 영향을 미친다는 것을 밝혔다는데 의의가 있으며 또한, 이는 보육교사의 인성 요인을 고도화하는 방안 마련이 필요함을 시사한다. The purpose of this study is to examine the influence of childcare teachers' personality on praise leadership and teaching metacognition, and to provide basic data necessary to improve the personality of childcare teachers. The research participants surveyed 250 childcare teachers working at daycare centers in K city. For data analysis, descriptive statistics and multiple regression analysis were performed using the SPSS 26.0 (ver.) statistical program. Looking at the results, first, when using the 5-point Likert scale, childcare teachers’ personality was 3.88, praised leadership was 3.89, and the teaching metacognition level was 3.88. Second, among the personality factors of childcare teachers, communication, creative disposition, humor/wit, initiative self-achievement, and altruism had a significant effect on praise leadership in that order. Third, among the personality factors of childcare teachers, communication, creative disposition, initiative self-fulfillment, job performance attitude, and self-development had a significant effect on teachiog metacognition in that order. Therefore, this study is meaningful in that it revealed that some of the personality factors affect praise leadership and teaching metacognition, which suggests that it is necessary to develop measures to improve the personality factors of childcare teachers.
SemiMemes: A Semi-supervised Learning Approach for Multimodal Memes Analysis

The prevalence of memes on social media has created the need to sentiment analyze their underlying meanings for censoring harmful content. Meme censoring systems by machine learning raise the need for a semi-supervised learning solution to take advantage of the large number of unlabeled memes available on the internet and make the annotation process less challenging. Moreover, the approach needs to utilize multimodal data as memes’ meanings usually come from both images and texts. This research proposes a multimodal semi-supervised learning approach that outperforms other multimodal semi-supervised learning and supervised learning state-of-the-art models on two datasets, the Multimedia Automatic Misogyny Identification and Hateful Memes dataset. Building on the insights gained from Contrastive Language-Image Pre-training, which is an effective multimodal learning technique, this research introduces SemiMemes, a novel training method that combines auto-encoder and classification task to make use of the resourceful unlabeled data.
PharmaLedger: Blockchain-Enabled Healthcare: Privacy-Enhancement with Blockchain

Topics such as supply chain, clinical trials and health data will be explored along with how blockchian can exist in harmony with GDPR.
JOURNAL ARTICLE WRITING TRAINING FOR ENGLISH TEACHERS IN LANDAK DISTRICT

As part of the Tri Dharma of Higher Education, the community service team of lecturers in the Master of English Education Study Program in 2022 conducted Community Service activities in Landak Regency, one of the regencies in West Kalimantan. This community service aimed to train English teachers regarding journal article writing that can be published in local and national journals. This activity was done based on good intentions to help teachers in Landak Regency, which is relatively far from the Capital City of West Kalimantan Province, to improve their professionalism as teachers who not only teach but also publish and manage their ranks. The current community service targeted English teachers at the elementary, junior high, and high school levels using the method of exposure accompanied by questions and answers with resource persons who have experience in the world of research and publication in both national and international journals. The number of participants who participated in this activity was 36, consisting of English teachers at all levels of education. Although the targets were elementary to high school teachers, there were two lecturers. The result of the community service indicates that the training was helpful for the participants.
Understanding the Impact of Customer Reviews Through Coded Content Analysis

This study will analyze customer reviews using a coded content analysis style to understand the impact of consumer language and ratings in relation to their purchases. More specifically, this paper will examine an Amazon product, a refurbished iPhone 7, and perform a coded content analysis to analyze customer reviews in terms of condition, functionality, and satisfaction. Furthermore, statistical tests will be applied in order to understand the relationship between the language used by consumers reviews and their rating of the product, along with whether that has an impact on the sales of the product. From these results, the author will discuss real-life applications for how companies can understand the significance of customer reviews and how they can encourage consumers to leave positive reviews to benefit their company.
Finite-key analysis of decoy model semi-quantum key distribution based on four-state protocol

Semi-quantum key distribution allows a full quantum user Alice and a classical user Bob to share a pair of security keys guaranteed by physical principles. Semi-quantum key distribution is proposed while verifying its robustness. Subsequently, its unconditional security of semi-quantum key distribution system is verified theoretically. In 2021, the feasibility of semi-quantum key distribution system based on mirror protocol was verified experimentally. However, the feasibility experimental system still uses the laser pulse with strong attenuation. It has been proved in the literature that the semi-quantum key distribution system still encounters the risk of secret key leakage under photon number splitting attack. Therefore, the actual security of key distribution can be further reasonably evaluated by introducing the temptation state and conducting the finite-key analysis in the key distribution process. In this work, for the model of adding one-decoy state only to Alice at the sending based on a four state semi-quantum key distribution system, the length of the security key in the case of finite-key is analyzed by using Hoeffding inequality, and then the formula of the security key rate is obtained. It is found in the numerical simulation that when the sample size is &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {10}^{5} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20230849_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20230849_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, the security key rate of &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ {10}^{-4} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20230849_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20230849_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, which is close to the security key rate of the asymptotic limits, can be obtained in the case of close range. It is very important for the practical application of semi-quantum key distribution system.
Staircase Recognition Based on Possibilistic Feature Quality Assessment Method

In the decision-making systems, sensors data are often affected by diversity types of imperfections which significantly reduce system performance. Thus, data quality assessment has become a primordial step in data learning process. In this work, feature quality assessment approach is used to select only useful and reliable information from those depth data in order to improve the performances of the staircase recognition system for the visually impaired. Possibility theory is utilized as a tool to deal with imperfect data and to represent knowledge. The developed feature quality assessment method is composed of two main steps. In the first step, the extracted features are classified in different quality levels. In the second step, an optimal feature subset is determined to discriminate between ascending and descending stairs. The proposed approach has experimentally shown to be very valuable with depth data, acquired from an ultrasonic sensor and a LiDAR sensor. The performance of the proposed approach has evaluated based on the classification accuracy which reached $$94.7\%$$ using the ultrasonic sensor dataset and $$92.42\%$$ using the laser rangefinder dataset.
Data Mining berbasis Nearest Neighbor dan Seleksi Fitur untuk Deteksi Kanker Payudara

Detecting breast cancer in early stage is not straightforward. This happens because biopsy test requires time to determine whether the type is benign or malignant. Data mining algorithm has been widely used to automate diagnosis of a disease. One of popular algorithms is nearest neighbor based because of its simplicity and low computation. However, too many features can cause low accuracy in nearest neighbor based models. In this research, nearest neighbor based with feature selection is developed to detect breast cancer. Conventional k-Nearest Neighbor (KNN) and Multi Local Means k-Harmonic Nearest Neighbor have been chosen as nearest neighbor based models to experiment. The feature selection method used in this study is filter based, namely Correlation based, Information Gain, and ReliefF. The experimental result shows that the highest recall metric of MLM-KHNN and Information Gain is 94% with 5 features. In brief, MLM-KHNN algorithm with Information Gain can increase the recall of the prediction of breast cancer compared with the conventional K-NN algorithm and have been deployed into website using Streamlit such that the model can be used to detect breast cancer from chosen Wisconsin dataset features.
Requirements Modeling

Industrial applications and research studies report that organizations using machine learning (ML) solutions may be at risk of failure or they could fall short of business objectives. One serious issue that is often neglected is meeting the specific requirements of machine learning-driven software systems (MLD-SS). The use of a variety of information technologies, integration methods and tools, and domain-specific processes can add to this complexity. The data-driven and black-box nature of ML may be another great challenge. Therefore, there is a clear need for adopting a stakeholder-centered approach to the requirements engineering (RE) of MLD-SS. Use case modeling (UCM) can make requirements simpler and understandable by all stakeholders, allow better communication of ideas and provide support to testing, validation, and verification processes. In this chapter, a ten-step RE method and a four-step UCM method are proposed for MLD-SS, and then these methods are applied in a real case study of a university hospital.
The Effectiveness of Multiple Representation Electronic Book Design in Improving Critical Thinking Skills and Creative Thinking Skills of High School Students

This study aims to determine the effectiveness of multiple representation electronic books developed in improving critical and creative thinking skills of high school students. The research method is development research (R&amp;D) with the ADDIE model, effectiveness testing is carried out at the implementation stage with a pretest-posttest control group design. The critical thinking skills test instrument is in the form of multiple-choice questions and creative in the form of essay questions. The data analysis technique uses descriptive statistics. The test sample consisted of 60 students of class XI divided into the experimental group and the control group. The results showed that the N-gain value for the critical thinking skills of the experimental group was 0.62 and the control group was 0.35, the experimental group's creative thinking skills were 0.66 and the control group was 0.33. The statistical test results obtained that there was a significant difference between the experimental group and the control group. The effectiveness of the e-book is categorized as having a large effect, the effect size of critical thinking skills is 1.438 and creative is 2.219. Student responses to the developed e-book were positive, where almost all students agreed with the statements presented.
A Robust Image Encryption Scheme Based on Block Compressive Sensing and Wavelet Transform

In this paper, a modified robust image encryption scheme is developed by combining block compressive sensing (BCS) and Wavelet Transform. It was achieved with a balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete wavelet transform (DWT) domain, and the coefficient vectors are confused using the coefficient random permutation strategy and encrypted into a secret image by compressive sensing. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme. The compressive encryption model using BCS with Walsh transform as sensing matrix and WAM chaos system, the scrambling technique and diffusion succeeded in enhancement of secure performance.
Annotation and Joint Extraction of Scientific Entities and Relationships in NSFC Project Texts

<p id="C1">Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China (NSFC) in 2018-2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the BiLSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities (not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.
TridentCap: Image-Fact-Style Trident Semantic Framework for Stylized Image Captioning

Stylized image captioning (SIC) aims to generate captions with target style for images. The biggest challenge is that the collection and annotation of stylized data are pretty difficult and time-consuming. Most existing methods learn massive factual captions or additional stylized bookcorpus independently to assist in generating stylized caption, which ignore core relationships between existing image-fact-style trident data. In this paper, we propose a novel image-fact-style trident semantic framework TridentCap for stylized image captioning, which includes an image-fact semantic fusion encoder (SFE) and a trident stylization decoder (TSD). Unlike existing methods, we directly mine the core relationship in image-fact-style trident data and use factual semantic and image to build cross-modal semantic feature space, achieving the coherence between image and text. Specifically, SFE aims to learn the image-related prior language knowledge information from factual text and leverage fine-grained region-level semantic correlations of image and factual text to achieve cross-modal semantic information alignment and integration. TSD is designed to decouple the dual-source fused semantic feature based on the target style to achieve stylized caption generation. In addition, we design a pseudo labels filter (PLF) to obtain and expand massive image-fact-style trident data by building pseudo stylized annotations for all image-fact data in traditional caption datasets, which can further strengthen stylized caption learning. It is a generic algorithm to solve the problem of insufficient data and can be used into any existing stylized caption models. We conduct extensive experiments on SentiCap and FlickrStyle datasets, which achieve consistently improvement on almost all metrics. Our code will be released at: https://github.com/WangLanxiao/TridentCap_Code.
Autofocusing for Synthetic Aperture Imaging Based on Pedestrian Trajectory Prediction

Occlusions and complex backgrounds are common factors that hinder many computer vision applications. In a street scene, the challenge of accurately predicting pedestrian trajectories comes from the complexity of human behavior and the diversity of the external environment. It is difficult, if not impossible, to extract relevant information to accurately predict pedestrian trajectories in dynamic scenes. Synthetic aperture imaging (SAI) uses an array of cameras to mimic a camera with a large virtual convex lens by projecting images of a scene from different views onto a virtual focal plane. It is commonly used to reconstruct occluded objects, and in a street scene, can provide observation of pedestrians occluded by other objects and pedestrians. In this paper, we propose a joint prediction method based on autofocusing of SAI to predict pedestrian trajectories in dynamic scenes. The main contributions of this paper include: 1) The task of pedestrian trajectory prediction in dynamic scenarios is redefined as pedestrian trajectory prediction and SAI autofocusing from a practical but more challenging perspective. 2) The proposed method is based on an existing SAI-based method to extract information in heavily occluded views, which can obtain more accurate results but with less computational cost and without using other sensors such as LiDAR or depth cameras. 3) A new pedestrian trajectory prediction model, an attention-based trajectory prediction variational autoencoder (ATP-VAE), is proposed to extract complex human behavior and social interactions in dynamic scenes through a new Intention Attention Unit. The experimental results on multiple public datasets show that the proposed method achieves state-of-the-art results in the first-person perspective and in aerial view.