Reviewerly/exHarmony huggingface dataset:
Spiritual and moral aspects of personality and reflexivity in the context of research on spiritual capacities

The article reflects the results of studying the relationship of the spiritual and moral aspects: spiritual and moral qualities, altruism, striving for meaning (as sub-components of the moral component of spiritual capacities) with reflexivity (considered as a sub-component of the mental component of spiritual capacities). It is noted that there are different types of reflection: productive and unproductive. The purpose of the study is to find out whether there is a connection between aspects of the spiritual and moral sphere with reflexivity (its different types). On the basis of a sample of 274 people, a significant positive relationship of the aspects of spiritual and moral sphere of personality with a productive type of reflection (systemic reflection) was established and a significant negative connection or lack of connection with unproductive types of reflection (introspection and quasi-reflection) was found, which makes it possible to identify the sub-component of the mental component of spiritual abilities "reflexivity" with productive (systemic) reflection.
Under the Digital Background Marzano’s New Taxonomy of Educational Objectives: Conception of Deep Learning Analysis

Under the digital background, Marzano’s New Taxonomy of Educational Objectives proposes a new taxonomy for classifying educational objectives, which emphasizes teaching higher levels of thinking and deals with three knowledge domains, namely, the domain of information, the domain of mental procedures, and the domain of psychomotor procedures. This study compares and analyzes the classification of different educational goals, the New Taxonomy is underlain by the conception of deep learning, which discloses the mechanism of how knowledge, thinking and emotions interact with each other and develop through learning in the information flow process. The core of deep learning in the New Taxonomy is to advocate coordinated development of cognition, meta-cognition, and self-system. The ultimate goal of deep learning is to improve the self-system by teaching high levels of thinking and developing students’ metacognition.
Exploration of the Path for School Principals to Promote the Construction of Artificial Intelligence Curriculum – Based on Case Study

Popularizing artificial intelligence education in primary and secondary schools is an important measure to cultivate talents in the intelligent era. At present, the AI courses in primary and secondary schools are mainly based on information technology courses, makers, STEAM, programming and other elective courses or school-based courses. This promotion method further strengthens the leading position of principals in the process of AI curriculum construction. However, how primary and secondary school principals can effectively promote the high-quality and sustainable development of school AI curricula has not attracted enough attention. Research selected artificial intelligence education to be carried out more than three years and widely recognized school, using more case study methods, from the “curriculum consciousness, curriculum planning, curriculum management, curriculum evaluation, curriculum efficiency” perspective, following the principle of triangle mutual certificate, concise the primary and secondary school principals to promote the effective path of artificial intelligence curriculum.
An Empirical Study on Impact of Guidance Experience on Supervisor Identification Based on Structural Equation Modeling

The identification of postgraduates to their supervisor will affect their experience of instruction and the relationship of instruction. Analyzing the guidance experience (GE) by postgraduates and its influence on supervisor identification (SI) is helpful to establish a better supervisor-postgraduate relationship (SPR). Based on the definition of informal organization and the theory of organizational identification, this study uses the structural equation model method in data mining to explore the influence of GE on the recognition of postgraduates’ supervisors from three dimensions: academic and research guidance (ARG), moral cultivation (MC), employment assistance and management (EAM). The results of 399 valid questionnaires of postgraduates show that: the MC of supervisor has a significant impact on the SI, the GE significantly positively correlated with SPR, and influences on SI via partial mediation of SPR. The students who highly agree with their supervisor have the highest evaluation on their supervisor’s research guidance. The factors resulting in the maximum difference in the SI are concentrated on the personality and attitude, followed by the task, the academic plan, and research direction assigned by the supervisor. Supervisors should pay more attention to moral education, improve SPR and manage postgraduates appropriately rather than solely focusing on academic research. These research results provide a theoretical basis for the optimization of GE and provide ideas for the build positive SPR.
Unveiling the Golden Thread: Unmasking the Power of Blockchain for Sustainable Consumption

There is limited empirical support for understanding how blockchain transparency shapes consumers’ sustainable consumption. Therefore, this study investigates the impact of blockchain transparency on consumers’ sustainable consumption choices. This study delves into the impact of blockchain transparency on consumers’ intentions to purchase sustainable products, a topic of increasing relevance in contemporary markets. Employing a novel blockchain-based app scenario in the fashion apparel industry, we surveyed 282 European consumers. Robust data analysis using the partial least squares method reveals that blockchain transparency signals product sustainability trustworthiness and effectively satisfies consumer concerns, resulting in increased purchase intent and positive word of mouth. Notably, we uncover a moderating effect of information seeking on the trust-consumer satisfaction relationship. Our study contributes pivotal insights to the evolving discourse on blockchain and sustainable consumption, offering valuable implications for both academia and industry.
AMF-RetinaNet: Improved RetinaNet for Transmission Line Fittings Detection Based on Attention Mechanism and Multi-Scale Feature Fusion

The detection of transmission line fittings is extremly significant for keeping the operation of the power grid safe and stable. In order to avoid the occurrence of poor detection results caused by the complicated background environment and different shapes and sizes of the detected targets in aerial photographs of transmission lines, an enhanced RetinaNet method for detecting transmission line fittings is proposed. The proposed approach incorporates an attention mechanism and multi-scale feature fusion, called AMF-RetinaNet. First, a RetinaNet detection network has been built. To enhance the significance of the detected fittings, a spatial and channel convolutional attention model is introduced to suppress complicated background interference. Then, the FPN structure of the original RetinaNet detection structure Neck is changed to the FPN+PAN structure, thereby obtaining more comprehensive multi-scale features. Finally, experiments on the self-built fittings dataset verified the feasibility of AMF-RetinaNet. Experimental results demonstrate that AMF-RetinaNet can effectively detect multi-scale fitting targets on transmission lines, even in challengin environment. Compared with standard RetinaNet, its average detection accuracy mAP has increased by 4.81%.
A Performance-Based Assessment Approach for Cloud Service Provider Selection

More and more companies are increasingly using cloud services. Accordingly, the decision for a cloud service provider is crucial for the potentially added value of the cloud service to the company, which is why the selection should be subject to a well-planned process, and the final decision must be made carefully. Since cloud service providers make resources available via the internet, performance parameters are important for assessing cloud service providers and play an important role in the decision-making process. Therefore, this paper addresses the question of which network performance metrics are relevant for selecting a cloud service provider and how these metrics can be collected and evaluated. It provides and instantiates an assessment method that can be used as guidance for cloud service provider selection. Thereby, the results show the possibility of testing and evaluating technical instead of merely qualitative aspects of infrastructure provided by cloud service, which is a contribution to existing literature in the field.
A Portfolio Adjusting Model with Triangular Intuitionistic Fuzzy Return

To fit the changes in the investment process, a portfolio adjusting method with triangular intuitionistic fuzzy return is put forward. The expected return rate and risk of the portfolio are characterized by mean value and variance of triangular intuitionistic fuzzy number. Then, an intuitionistic fuzzy portfolio adjusting model is established by minimizing the variance risk of portfolio and ensuring the expected return greater than some aspired return levels. Finally, an application example of stock portfolio is given to demonstrate the practicability of intuitionistic fuzzy portfolio adjusting model.
Lost on the Way: “The Walking Man” by Alberto Giacometti and Its Recontextualization by Piotr Stepnowski as a Metaphor for the Human Condition

The main idea of the article is Henry David Thoreau’s conviction that being lost on the way, losing one’s way and, consequently, coming to a stop enables one to truly explore the present location and to establish the destination one wishes to reach. This perspective allows to observe the situation of a modern man and pose questions related to their actions to date. The interpretative material through which these problems are realized are two contemporary sculptures, treated as metaphors of the human condition: Albert Giacometti’s Walking Man and Piotr Stepnowski’s Walking Man (a form of recontextualization of Giacometti’s sculpture).
Implementation Of Fuzzy Multi-Criteria Decision Making To Design An Expert System For Prediction Of Digestive Diseases At Dogs

A dog is a kind of animal that is often looked after at home. Except for being known as a human's friend, a dog is also able to be trained to do many things. The age of the dog depends on the type of race. If it's fed well, exercises, and visits the doctor regularly, a dog can live longer. Dogs are very sensitive to certain diseases; one of them is intestinal disease. An expert system of a dog's digestion is built for the dog's lover so that they know exactly what's disease, which relies on the appearance of the symptoms. The symptoms of the different intestinal diseases look almost similar. For this reason, we need an appropriate method to get a precise result. Fuzzy multiple-criteria decision-making can help make the right decision that has some consideration points. This method is also equipped with optimal ultimate selections, which leads to a very precise result. The goal of this application is to know the right kind of disease and its therapy. This study has been validated by matching the results of the application with the doctor's diagnosis. The compatibility level is 80%.
Sentiment Analysis Regarding Kanjuruan Stadium Polemics Based on Public Opinion Through Twitter Social Media with SVM Classifier Method

Football fans are individuals who promote, motivate, and inspire football. Players of football clubs have both positive and negative fanaticism in both the real world and social media, especially on Twitter. Twitter is one of the communication media. Attracting people worldwide, Twitter saw a record increase in global users, with 313 million monthly active users in 2016 alone; the majority accessed Twitter through mobile devices, accounting for 82 percent of users. Due to the multitude of users tweeting, the latest news and comments become significant worldwide. What happens becomes the main topic, and comments received from many users trigger trending topics on Twitter. This research aims to develop a classification model to predict whether tweets from stadium events are positive or negative from fan perspectives. The classification model is based on a Twitter dataset, and sentiment analysis of tweets was conducted using the Support Vector Machine (SVM) algorithm. The next step involved preprocessing, including case-folding, cleansing, translation to English, and sentiment labeling using VADER. Subsequently, in the preprocessing step 2, tokenization, stopwords, and stemming were applied. For modeling, classic algorithms such as Naïve Bayes and Support Vector Machine were used. The highest accuracy, 87.77%, was achieved using the Support Vector Machine (SVM) algorithm.
Measuring Latency-Accuracy Trade-Offs in Convolutional Neural Networks

Several systems that employ machine learning models are subject to strict latency requirements. Fraud detection systems, transportation control systems, network traffic analysis and footwear manufacturing processes are a few examples. These requirements are imposed at inference time, when the model is queried. However, it is not trivial how to adjust model architecture and hyperparameters in order to obtain a good trade-off between predictive ability and inference time. This paper provides a contribution in this direction by presenting a study of how different architectural and hyperparameter choices affect the inference time of a Convolutional Neural Network for network traffic analysis. Our case study focus on a model for traffic correlation attacks to the Tor network, that requires the correlation of a large volume of network flows in a short amount of time. Our findings suggest that hyperparameters related to convolution operations—such as stride, and the number of filters—and the reduction of convolution and max-pooling layers can substantially reduce inference time, often with a relatively small cost in predictive performance.
CHALLENGES TO HIGHER EDUCATION IN SERBIA – A CONTRIBUTION TO THE PUBLIC DEBATE ON HIGHER EDUCATION REFORM

Higher education, as an area of great public interest, is the subject of a series of state actions, laws and allocation of financial resources. Reform/s in the field of higher education assume that we have identified problems in higher education and that we have criteria based on which we will evaluate reform solutions and opt for the one/s that best meet those criteria. The intention of this text is to contribute to the identification and understanding of some of the problems that public policies in the field of higher education should address and to offer criteria for evaluating the proposed solutions. As the basic problems of higher education (in Serbia) we have identified - the enlargement of the student population and the demand for quality control arising from the need to standardize every "serial" production, the need to expand the coverage of underrepresented social groups, the need for financial and other support for underrepresented social groups, which arise from the combination of enlargement and inclusiveness of higher education, the increase in the diversity of the student population in terms of abilities, aspirations, motivation and value orientations, and the need for innovations in teaching methods in higher education, which results from this.
Pelatihan Pengelasan dan Pembuatan Alat Penunjang Laboratorium Pengelasan di Sekolah Menengah Kejuruan Negeri Pertambangan Bungku

Higher education has an important role in the Tri Darma, namely participating in truly educating the lives of the nation and state in society. One step is to make humans no longer objects of development but subjects of development. As a development subject, abilities and skills in mastering science and technology are certainly required. The use of welding technology has developed and is widely used and exploited in the wider community, especially in meeting industrial needs. One of the welding methods is Shielded Metal Arc Welding (SMAW), namely welding using an electric arc. The aim of this activity is to increase the welding competency of Bungku Mining Vocational School students by adding special skills in the form of SMAW welding and providing encouragement and assistance so that Bungku Mining Vocational School students are able to provide welding equipment that supports learning in the laboratory. The benefit of this activity is to provide knowledge and train skills in SMAW welding so that it can be used to develop laboratory facilities.
High-dimensional Encoding in the Round-Robin Differential-Phase-Shift Protocol

In quantum key distribution (QKD), protocols are tailored to adopt desirable experimental attributes, including high key rates, operation in high noise levels, and practical security considerations. The round-robin differential phase shift protocol (RRDPS), falling in the family of differential phase shift protocols, was introduced to remove restrictions on the security analysis, such as the requirement to monitor signal disturbances, improving its practicality in implementations. While the RRDPS protocol requires the encoding of single photons in high-dimensional quantum states, at most, only one bit of secret key is distributed per sifted photon. However, another family of protocols, namely high-dimensional (HD) QKD, enlarges the encoding alphabet, allowing single photons to carry more than one bit of secret key each. The high-dimensional BB84 protocol exemplifies the potential benefits of such an encoding scheme, such as larger key rates and higher noise tolerance. Here, we devise an approach to extend the RRDPS QKD to an arbitrarily large encoding alphabet and explore the security consequences. We demonstrate our new framework with a proof-of-concept experiment and show that it can adapt to various experimental conditions by optimizing the protocol parameters. Our approach offers insight into bridging the gap between seemingly incompatible quantum communication schemes by leveraging the unique approaches to information encoding of both HD and DPS QKD.
Investigation of influence of objective function valley ratio on the determination error of its minimum coordinates

Objectives . A valley is a region of an objective function landscape in which the function varies along one direction more slowly than along other directions. In order to determine the error of the objective function minimum location in such regions, it is necessary to analyze relations of valley parameters. Methods . A special test function was used in numerical experiments to model valleys with variables across wide ranges of parameters. The position and other valley parameters were defined randomly. Valley dimensionality and ratio were estimated from eigenvalues of the approximated Hessian of objective function in the termination point of minimum search. The error was defined as the Euclidian distance between the known minimum position and the minimum search termination point. Linear regression analysis and approximation with an artificial neural network model were used for statistical processing of experimental data. Results . A linear relation of logarithm of valley ratio to logarithm of minimum position error was obtained. Here, the determination coefficient R 2 was ~0.88. By additionally taking into account the Euclidian norm of the objective function gradient in the termination point, R 2 can be augmented to ~0.95. However, by using the artificial neural network model, an approximation R 2 ~ 0.97 was achieved. Conclusions . The obtained relations may be used for estimating the expected error of extremum coordinates in optimization problems. The described method can be extended to functions having a valley dimensionality of more than one and to other types of hard-to-optimize algorithms regions of objective function landscapes.
IMPLEMENTASI KONSEP ROUTING PADA SISTEM PENGELOLAAN DATA OBAT DI PUSKESMAS

This research aims to implement the routing concept in the drug data management system at Community Health Centers (Puskesmas) to enhance efficiency and accuracy in drug distribution. As primary healthcare service centers, Puskesmas require a system that can optimize the drug data management process to provide better healthcare services to the community. The research methodology involves analyzing system requirements, designing a suitable routing concept, and implementing it into the drug data management system at Puskesmas. The routing concept considers various factors such as distance, time, and drug availability to determine the optimal route for drug distribution to different units or rooms within the Puskesmas. The expected outcomes of this research include a positive contribution to the efficiency of drug distribution at Puskesmas, reducing drug handling time, and minimizing the possibility of errors in drug stock management. The implementation of the routing concept in this system is anticipated to provide an effective and efficient solution to drug management at Puskesmas, ultimately improving the quality of healthcare services for the community.
Intelligent anomaly detection method for power and electricity data based on cubic exponential smoothing model

Abstract During the operation of the power system, abnormal electricity data are inevitably generated. How to efficiently detect and identify these abnormal data is a crucial component of power system state estimation, and it is also the foundation for the safety and stability of power system operation. Traditional anomaly detection methods only focus on specific power system characteristics, and there are problems with high computational complexity and low accuracy. To address the shortcomings of traditional methods for anomaly detection in power and electricity data, a smart anomaly detection method for power and electricity data based on a cubic exponential smoothing model is proposed. The cubic exponential smoothing model uses historical data to predict the current regional electricity consumption and then subtracts the predicted value from the true value to obtain the residual term. Finally, the DBSCAN density clustering algorithm is used to cluster the residual term, achieving the recognition of abnormal electricity data. We conduct an experimental comparison of regional electricity consumption data of a certain power grid. The results indicate that the proposed method has achieved good results in both detection rate and false alarm rate indicators in the intelligent detection of abnormal data in power and electricity consumption.
STRENGTHENING CYBERCRIME CONTROLAND INVESTIGATION PROTOCOLS FORSAFE GUARDING INDIAN CITIZENS.

This research article explores the current state of cybercrime in India and proposes comprehensive strategies to enhance control and investigation protocols for the protection of Indian citizens. As the digital landscape continues to evolve, so do the challenges posed by cyber threats. The paper aims to address key issues such as the rising incidence of cybercrimes, the need for advanced investigation techniques, and the importance of collaborative efforts among law enforcement agencies, government bodies, and the private sector.