Reviewerly/exHarmony huggingface dataset:
A Study on the Double Mediating Effect of School Psychosocial School Environment Satisfaction and Bonding with Teacher in the Effects of High School Students' Academic Stress on Happiness

In this study, based on previous studies, in order to verify the double mediating effect of psychosocial school environment satisfaction and bonding with teachers in the effect of high school students' academic stress on happiness.Data from 325 high school students who responded to the supplementary survey were used.First, high school students' academic stress was somewhat lower than the median value, but their psychosocial school environment satisfaction, bonding with teachers, and happiness were quite high.Second, the higher the academic stress of high school students, the lower the happiness, but the intensity was somewhat lowered by the satisfaction of the psychosocial school environment and the bond with teachers.In other words, in the relationship between academic stress and happiness of high school students, the double mediating effect of satisfaction with the school environment and bonding with teachers was verified.Based on the results of this study, a plan to improve the satisfaction of the psychosocial school environment and the bond with teachers was proposed to reduce the negative influence of high school students' academic stress on their happiness.
Understanding the inclination of South Indian nursing graduates in using mobile learning applications.

Clinical-based mobile learning courses have great demand among the nursing graduates as learners look for possibilities to update skills. The present study explores the feasibility, familiarity, utility, and attitude of nursing graduates of South India toward mobile learning applications (m-apps).An online-based cross-sectional descriptive survey was conducted in May 2021 among the South Indian nursing graduates of Tamil Nadu and Kerala, using a questionnaire consisting 49 items categorized under six sections with items related to socio-demographic information; m-app usage; online learning experience; preference in using m-apps for learning purposes before COVID-19 and during COVID-19; students' engagement through e-learning; and anxiety related to online assessment. Descriptive and inferential (ANOVA, Chi-square, and t test) statistical data analysis were done using SPSS version 23.A total of 447 student nurses responded. The result shows that most of them, 96% (432) used android phones and 94% (422) owned a mobile. Age was highly influencing mobile learning application (m-learning apps) usage; students of less than 20 years of age used applications more frequently and possessed more educational applications. Majority of them, 84% (377) of them had started using m-learning apps only after COVID. 57.7% (249) commonly used m-learning apps for acquiring nursing knowledge resources, nursing exam preparatory, and drug resources. Students rated high for the interactive nature of these m-learning apps, while abundant learning materials and usage with ease were rated as other attractive features. Sixty-six percent (305) had mostly downloaded these apps from Google Play Store.The findings would help the m-learning application developers to offer customize solutions to address learning gaps that prevail among the South Indian nursing graduates and help with sustainable growth.
FEATURES OF THE INTERRELATION OF SELFASSESSMENT AND LOCUS OF CONTROL IN HIGHER EDUCATION OBTAINERS

The article presents a theoretical analysis and the results of an empirical study of the problem of the relationship between self-attitude and locus of control in students of higher education. The study of self-attitude of students of higher education showed that, in general, they have a positive attitude towards themselves, accept themselves as they are, but their self-interest, self-management, self-understanding, and autosympathy turned out to be insufficiently developed. Which indicates a relatively problematic attitude, the presence of an internal conflict. The study of the level of subjective control showed that externality prevails among the subjects in such areas of life as: general externality, externality in the area of failures; and internality, respectively, in the field of achievements, production, family, interpersonal relationships and health. That is, the researched students are ready to take responsibility where they really feel they have the power to control or change something. The analysis of the peculiarities of the relationship between self-attitude and the level of subjective control of the subjects allows us to state that the relationship between various aspects of self-attitude and the locus of control has been revealed. In general, the higher the level of self-esteem, self-acceptance and selfunderstanding, the more often a person is ready to take responsibility for achievements, failures, as well as for relationships – family, industrial, interpersonal. Thus, it can be stated that optimizing the attitude towards oneself, self-esteem, leads to increased responsibility for one’s own life. Key words: self-determination, locus of control, higher education student, personality.
End-to-end trustworthy ML for multidomain operations

In this article, we present SFMLOps, a Security Framework for Machine Learning Operations (MLOps), a comprehensive and novel approach to securing MLOps pipelines in multi-domain operations. SFMLOps can be used to benchmark security in mobile cyber-physical systems like quadruped reconnaissance robots, unmanned autonomous vehicles, and wearable brain-computer interfaces. Our framework examines and categorizes potential attack surfaces and threats within MLOps, offering countermeasures and their effectiveness for various aspects such as data storage, communication channels, model training, and model predictions. We provide security engineers practical guidance on developing secure MLOps pipelines. We introduced a secure pipeline design, MLPipeSec, based on a publisher-subscriber model and implemented on the JointForceNet+ Blockchain to ensure end-to-end trustworthiness across the MLOps pipeline. To evaluate the impact of security parameters on a multi-domain computer vision task, we compared several frameworks for their security and performance using the CIFAR-10 dataset. We also investigated Gossip Learning as a federated learning framework in conjunction with Google Cloud Platform and introduced a new federated learning model, VizFedML. Our experimental results demonstrate the efficacy of the SFMLOps framework and the MLPipeSec design in mitigating a range of vulnerabilities and weaknesses associated with MLOps, contributing to the development of more secure and robust machine learning systems.
The Role of Internet Communication in Consolidation of the Russian-Speaking Community in Brazil

The Internet is becoming one of the main platforms for communication, as well as an important source of information for people. It has a special role for migrants, since Internet communication can cover large geographical distances. The purpose of this study was to identify the features of the internet communication of the Russian-speaking youth abroad using the example of Brazil. Particular attention is paid to identifying the most popular Internet sites and the most relevant discussion questions. The results of an anonymous survey of Russian-speaking youth in Brazil act as the empirical basis of the study. The study showed that Internet communication plays an important role in strengthening communication within the Russian-speaking community and preserving common cultural values. Interestingly, there is a significant difference in how internet communication is perceived by those born in Brazil, and those who were born in Russia (USSR) and are a migrant in the first generation. The Internet acts here as a unique platform allowing these two groups of compatriots to interact and communicate. On the one hand, there is an exchange of experience and useful information about the host country, which can significantly facilitate adaptation for newly arrived migrants. On the other hand, they, in turn, give the “Russian Brazilians” an opportunity to get information about Russia, stimulate the interest in Russian language and culture. According to the results of the study, the compatriots who were born in Brazil were more interested in maintaining ties with their historical homeland.
WORKSHOP ANALISIS DATA KUANTITATIF DAN KUALITATIF UNTUK AKADEMISI DAN PROFESIONAL

Problems with data processing and analysis capabilities often hinder lecturers and students in producing research work. Likewise, employees in the financial sector perform data processing in order to support their tasks, namely being able to analyze data into strategic information for the company. The problem solving is carried out by the Master of Applied Economics Study Program, Faculty of Economics and Business, Atma Jaya Catholic University of Indonesia, Jakarta, by organizing a Quantitative and Qualitative Data Analysis Workshop for students, lecturers, and financial practitioners. The activity starts from July 19 to August 19, 2022. Learning media is held online through Zoom Meetings. The participants of this activity are lecturers and students from various institutions/universities throughout Indonesia, from the University of North Sumatra to the University of Ottow Geissler Papua, as well as from practitioners of financial institutions. The number of applicants for this Data Analysis Training is 558 participants. Workshop material is an introduction to regression analysis &amp; multivariate modeling (data stationarity,ARDL, ECM, cointegration), univariate modeling (ARMA / ARIMA), Volatility modeling (GARCH), Logistics Regression Analysis, VAR, Panel Data Analysis, data analysis with R Studio, Multivariate statistical analysis (PCA, Discriminant, Cluster Analysis) and path analysis (Path Analysis). Based on the evaluation of the participants, this workshop is very useful for them and it is hoped that there will be a follow-up workshop
An Efficient Real-Time Object Detection and Tracking Framework Based on Lidar and Ins

Obstacle detection and tracking is an integral part of the autonomous vehicle perception algorithm. Because most of the existing tracking algorithms have the problems of insufficient accuracy and poor real-time performance, an obstacle detection and tracking method based on LiDAR is proposed in this paper. Firstly, for the scene with the undulating ground in the environment, a twice-ground segmentation method based on plane fitting and scan line geometric features is proposed to accurately and robustly extract the high obstacle point cloud. Secondly, the density clustering algorithm is optimized, and a convex hull rectangular 3D bounding box fitting algorithm is proposed to detect obstacles. Finally, the Mahalanobis distance measurement feature is used to realize the data association between the previous and the current frame. And the interacting multiple model filter algorithm embedded in the unscented Kalman filter is used to estimate the state of the object optimally. Based on the public data set, the proposed algorithm improves the accuracy of tracking detection. After verification on the self-developed real vehicle experimental platform, the results show that the algorithm has good object tracking and correlation performance.
An exploration of project-driven cultivation of students’ innovative ability in college English teaching

Innovation ability is an integral part of students’ comprehensive ability. Compared with students majoring in engineering, students majoring in arts should pay more attention to the improvement of innovation ability in training. This paper expounds the significance of project driven in the cultivation of college students’ innovation ability, introduces the goal of cultivating students’ innovation ability in college English teaching, discusses the implementation path and optimization strategy of the project from three aspects of reading project, writing project and speech project, and explores the talent cultivation mode that conforms to the characteristics of college English teaching and acknowledges the social needs.
Pose ResNet: A 3D human pose estimation network model

The accurate estimation of 3D human pose is of great importance in many fields, such as human-computer interaction, motion recognition and automatic driving. We take 2D images as our research object in this paper, and propose a 3D pose estimation model called Pose ResNet. First, the model uses ResNet50 as the base network and introduces the attention mechanism CBAM to extract features. Then, a waterfall atrous spatial pooling (WASP) module is used to capture multi-scale contextual information from the extracted features to increase the receptive field. Finally, the features are input into a deconvolution network to acquire the volume heat map H, which is later processed by a soft argmax function to obtain the coordinates of the joints. The results show that the mean per joint position error (MPJPE) is 51.8mm, compared with other approaches, our method achieves better results.
An Unmanned Vehicle Location Algorithm Based on UWb Combined TOF and TDOA

Aiming at the problem of large positioning error when UWB trilateral positioning method is directly applied to unmanned vehicle, an improved UWB trilateral positioning algorithm is proposed in this paper. An accurate initial value is obtained by combining TDOA and TOF, and then it is used as the initial value of Taylor expansion. Finally, Kalman filtering is applied to the positioning value to improve the positioning accuracy of UWB positioning algorithm when it is used for unmanned vehicle positioning.
RANCANG BANGUN APLIKASI MEDIA JASA DESAIN LOGO DENGAN METODE WATERFALL BERBASIS WEBSITE

The rapid development of internet technology has a huge influence on people's daily lives. Internet technology provides convenience for people to convey information quickly, one of them through the media website. Website is one of the right choices to convey information because it is more accessible from various places and regions just by using the internet. Design website as a promotional media logo design services is aimed to be a solution to the lack of promotional media logo design services on CV Jenester. The results of the website design will be used as a promotional media logo design services to expand the reach of promotion to the public through the internet. The method used in the design and construction of the website is a waterfall method included in one of the SDLC (Software Development Life Cycle) methods that have several stages ranging from analysis, design, coding, testing, and support. The result of website design is the implementation directly to the internet through web hosting. Website design is expected to be a solution to the lack of media promotion of logo design services on CV. Jenester.
Healthcare Management System Using Blockchain

Abstract: The current healthcare system has traditionally relied on paper-based medical records or electronic medical records (EMRs) that are stored in centralized databases. This method, however, has shown to be ineffective and unreliable, causing major delays in patient care. Medical documents are frequently sent over email as a temporary fix; however, this approach is unsafe and unreliable. Data breaches or unauthorised access to sensitive patient information pose a serious danger and may have far-reaching repercussions for both patients and healthcare providers. Blockchain technology can provide a more secure, decentralized, and efficient way to store and share medical records. Blockchain offers a tamper-proof and auditable method to trace transactions while using cryptographic methods to secure the integrity and confidentiality of data, access to patient data and modifications. Patients, physicians, and hospitals may easily, anytime, anywhere, and with total safety and security, access medical records via a decentralised blockchain network. Patients have discretion over who gets access to their information and can allow healthcare professionals and organisations access to their medical records. We are employing a number of technologies, like MetaMask, Ganache, Web3JS, and Solidity, to develop a blockchain-based solution. Users may connect with blockchain networks via the browser plugin MetaMask, and a local blockchain development tool called Ganache offers a testing environment for blockchain-based apps. Programmers may interface with the Ethereum blockchain using Web3JS, a JavaScript framework, and Solidity, a language used to create smart contracts the blockchain of Ethereum. A centralised blockchain network that can safely store and distribute patient data is the predicted result of a blockchain-based system for medical record sharing. The system's interoperability and scalability may be ensured via integration with already-existing healthcare systems and databases. Medical record sharing may be automated with smart contracts, allowing for safe and auditable access based on predefined criteria and permissions.
Construction of Campus English Education Resources Information Platform Based on Internet of Things

In order to realize the application functions of collecting, processing, retrieving, sharing and reusing English teaching resources, this paper constructs an English education resource information platform based on the Internet of Things technology and smart campus construction technology. According to the construction requirements of the intelligent teaching resource service cloud platform, the service layer and application service layer of the software system are designed, and the system test results of the English education resource information platform are given. The test results show that when 300 concurrent users access, the average corresponding time for reading is 0.901 s, and the average corresponding time for writing is 2.82 s, both within the normal range. Through the survey, students and teachers have produced good feedback on the teaching effect of the English education resource information platform.
A Novel Evolutionary Approach for Neural Architecture Search

Convolutional Neural Networks (CNNs) have proven to be an effective tool in many real-world applications. The main problem of CNNs is the lack of a well-defined and largely shared set of criteria for the choice of architecture for a given problem. This lack represents a drawback for this approach since the choice of architecture plays a crucial role in CNNs’performance. Usually, these architectures are manually designed by experts. However, such a design process is computationally intensive because of the trial-and-error process and also not easy to realize due to the high level of expertise required. Recently, to try to overcome those drawbacks, many techniques that automize the task of designing the architecture neural networks have been proposed. To denote these techniques has been defined the term “Neural Architecture Search” (NAS). Among the many methods available for NAS, Evolutionary Computation (EC) methods have recently gained much attention and success. In this paper, we present a novel approach based on evolutionary computation to optimize CNNs. The proposed approach is based on a newly devised structure which encodes both hyperparameters and the architecture of a CNN. The experimental results show that the proposed approach allows us to achieve better performance than that achieved by state-of-the-art CNNs on a real-world problem. Furthermore, the proposed approach can generate smaller networks than the state-of-the-art CNNs used for the comparison.
Productive Inference of Convolutional Neural Networks Using Filter Pruning Framework

Deep neural networks have shown phenomenal performance in many domains including computer vision, speech recognition, and self-driving cars in recent years. Deep learning model’s high performance normally comes at the cost of computation time and significant size of the model. These factors ultimately become a bottleneck for the deployment of deep learning models on battery and memory constrained devices, for example embedded systems or mobile phones. Over the past few years network acceleration is a burning topic. To address this task of compressing deep learning models, in the past few years many researchers have come up with compression techniques like pruning. The authors, in this paper, have tried to implement the filter pruning technique on VGG 16 architecture by using a clustering methodology to compress and deploy the model on resource-constrained devices like smartphones. The authors have also aimed at improving the inference time of the model for fast and considerably accurate predictions.
Role of Institutional Quality, Financial Development, and Industrialization in Human Development of Selected Asian Countries

Human development has gained much significance in economies.This study highlight the role of institutional quality, financial development,and industrialization in human development in selected Asian countries. The human development index is used as a dependent variable. The study has used the GMM technique for this analysis. The study results demonstrate that institutional quality, financial development, industrialization, and trade openness have played a significant and positive role in determining the human development of selected Asian countries. It is recommended that the government should provide a more stable environment to encourage more production, investment, and trade to enhance the living standard and human development of the selected economies. It is concluded that financial development may improve human development. There is a need for more transparency and improved institutional quality for human development.
The Comparison of Attention Mechanisms with Different Embedding Modes for Performance Improvement of Fine-Grained Classification

Fine-grained image classification is one of the key basic tasks of computer vision. The appearance of traditional deep convolutional neural network (DCNN) combined with attention mechanism can focus on partial and local features of fine-grained images, but it still lacks the consideration of the embedding mode of different attention modules in the network, leading to the unsatisfactory result of classification model. To solve the above problems, three different attention mechanisms are introduced into the DCNN network (like ResNet, VGGNet, etc.), including SE, CBAM and ECA modules, so that DCNN could better focus on the key local features of salient regions in the image. At the same time, we adopt three different embedding modes of attention modules, including serial, residual and parallel modes, to further improve the performance of the classification model. The experimental results show that the three attention modules combined with three different embedding modes can improve the performance of DCNN network effectively. Moreover, compared with SE and ECA, CBAM has stronger feature extraction capability. Among them, the parallelly embedded CBAM can make the local information paid attention to by DCNN richer and more accurate, and bring the optimal effect for DCNN, which is 1.98% and 1.57% higher than that of original VGG16 and Resnet34 in CUB-200-2011 dataset, respectively. The visualization analysis also indicates that the attention modules can be easily embedded into DCNN networks, especially in the parallel mode, with stronger generality and universality.