Reviewerly/exHarmony huggingface dataset:
PHILOSOPHICAL ASPECTS OF FORMING ECOLOGICAL CULTURE IN YOUTH

In the article, it is emphasized that in the education system, the concept of eliminating the ecological danger in front of the society and transitioning to a sustainable path of development by eliminating the global, regional and local problems of ecological culture has been constantly changing in the human society for several thousand years.The main indicators of the scientific direction are the monitoring of changes in space and time, such as the use of nature and the amount of product production, the number of people, the scale of use of natural resources, the amount of anthropogenic pressure on nature, the balance of the ecological and ethno-ecological landscape necessity is analyzed.
REPLACEMENT OF INFORMATION TECHNOLOGIES IN THE ORGANIZATION OF EXTRA-CURRICULAR ACTIVITIES IN GENERAL SECONDARY SCHOOLS (IN THE EXAMPLE OF TEACHING NATURAL SCIENCES)

In this article, the role of extracurricular activities in improving the educational experience of students in general secondary schools, their importance, and the advantages of using information technologies are shown.
THE USE OF ARTIFICIAL INTELLIGENCE-BASED CHATBOTS IN THE EDUCATIONAL SYSTEM

The integration of artificial intelligence (AI) technologies into education has shown promising opportunities for revolutionizing the learning process. ChatGPT, a conversational artificial intelligence model developed by Open AI, has attracted considerable attention as a potential tool for educational purposes. This article is aimed at exploring the potential of using AI as a learning tool in educational institutions. Thanks to a literature review and analysis of relevant articles, including "How to use ChatGPT as a Learning Tool" by Ashley Abramson (2023) and "ChatGPT: Educational Friend or Foe?" Katie Hirsch-Pasek and Elias Blinkoff (2023), this study examines various aspects of the usefulness, benefits and limitations of ChatGPT in facilitating learning. The data obtained emphasize the importance of teachers' understanding of ChatGPT's capabilities and the need for effective integration strategies to maximize its educational potential.
Phase Unwrapping for Phase Imaging Using Plug-and-Play Proximal Algorithm

Phase unwrapping (PU) is essential for various scientific optical applications. This process aims to estimate continuous phase values from acquired wrapped values, which are limited to the interval (-π,π]. However, the PU process can be challenging due to factors such as insufficient sampling, measurement errors, and inadequate equipment calibration, which can introduce excessive noise and unexpected phase discontinuities. This paper presents a robust iterative method based on the plug-and-play (PnP) proximal algorithm to unwrap two-dimensional phase values while simultaneously removing noise at each iteration. Using a least-squares formulation based on local phase differences and reformulating it as a partially differentiable equation, it is possible to employ the fast cosine transform to obtain a closed-form solution for one of the subproblems within the PnP framework. As a result, reliable phase reconstruction can be achieved even in scenarios with extremely high noise levels.
Developing linguistic tools of thematic search in library information systems

Within the R&amp;D program “Information support of research by scientists and specialists on the basis of RNPLS&amp;T Open Archive – the system of scientific knowledge aggregation”, the RNPLS&amp;T analyzes the use of linguistic tools of thematic search in the modern library information systems and the prospects for their development. The author defines the key common characteristics of e-catalogs of the largest Russian libraries revealed at the first stage of the analysis. Based on the specified common characteristics and detailed comparison analysis, the author outlines and substantiates the vectors for enhancing search inter faces of e-catalogs. The focus is made on linguistic tools of thematic search in library information systems; the key vectors are suggested: use of thematic search at different search levels with the clear-cut level differentiation; use of combined functionality within thematic search system; implementation of classification search in all e-catalogs; hierarchical representation of classifications; use of the matching systems for classification information retrieval languages, and in the long term classification and verbal information retrieval languages, and various verbal information retrieval languages. The author formulates practical recommendations to improve thematic search in library information systems.
RESEARCH PROGRESS IN THE SPLICING AND RESTORATION OF ARTIFACT FRAGMENTS BASED ON POINT CLOUD

Abstract. Due to environmental reasons, most of the artifacts are fragmented, and the surface information of the artifacts is also blurred. The traditional method of repairing artifacts mainly relies on archaeologists to manually repair them, using fragment features to compare each fragment one by one, this method will cause secondary damage to the fragments. Based on computer technology, virtual restoration of artifacts fragments can obtain the latest unearthed data of artifacts quickly, preserve digital information of artifacts, achieve permanent preservation, and provide prior knowledge for subsequent artifacts restoration. Point cloud data is widely used in artifacts virtual restoration technology due to its good depth of information. This paper takes ceramics, bronzes, Terra-cotta Warriors, and other individual artifacts fragments as the main research object, and the point cloud data obtained by 3D laser scanner as the main research data. It comprehensively classifies and summarizes the work of computer artifacts splicing in recent years.
AN IMPROVED IMAGE REGISTRATION ALGORITHM FOR THERMAL INFRARED AND PANCHROMATIC IMAGE BASED ON GEOMETRIC STRUCTURAL PROPERTIES

Abstract. In order to improve the image registration accuracy of heterogeneous remote sensing images with large radiation differences, an improved image registration algorithm for thermal infrared and panchromatic images is proposed. This method uses the phase consistency of intensity and direction to construct a geometric structure feature descriptor called the Histogram of Oriented Phase Congruency (HOPC) for image registration. It employs the Curvature Scale Space (CSS) corner detection to concentrate and extract feature points in contour areas, and defines a similarity metric (called HOPCn) based on the Euclidean distance between the descriptors for high-precision heterogeneous images registration. Five sets of panchromatic and thermal infrared images were selected for verification. The results show that the improved algorithm can match the homonymy points around the image contour, and the Correct Matching Rate (CMR) is improved by 2.7% and the Root Mean Square Error (RMSE) is improved by 3.7% compared to the original algorithm. This proves that the proposed method has good robustness under conditions of large radiation differences.
Optimizing criterion for the upper limit of the signal response of brain neurons

In a model of signal transmission between brain neurons, the Lyapunov functions associated with the “no signal” solution are positive and have a negative derivative with respect to the response. The solution is stable for a response range. Noise added to signal transmission and response enhances stability by allowing the system to escape tricky equilibria. It amplifies weak signals, improves detection and distinction of significant signals from background noise, and generates appropriate and adaptive responses to detected signals. It causes random fluctuations, allowing more parameter values to be tried out and thus optimizing the behavior of the system, enabling it to transmit and respond effectively to signals in the presence of the variability inherent in biological networks. The deterministic model is thus enhanced by its stochastic extension.
IvyRedaction: Enabling Atomic, Consistent and Accountable Cross-Chain Rewriting

Blockchain rewriting has become widely explored for addressing data deletion requirements, such as error data deletion, space-saving, and compliance with the “right-to-be-forgotten” rule. However, existing approaches are inadequate for handling cross-chain redaction issues, issues, in facing with the increasing need for inter-chain communication. In particular, transaction rewriting on a blockchain might have relevant effects on the states of other blockchains. The cross-chain interoperability results in inter-chain transactions with more complex dependency relations. The issues pose new challenges to achieve rewriting consistency, for example, ensuring the rewriting of related transactions when a transaction is being modified, and achieve atomic rewriting, whereby two cross-chain transactions must either all, or neither, be processed. This paper introduces a cross-chain solution IvyRedaction, with an emphasis on customizing a decentralized intermediary for generating and maintaining global cross-chain redaction states and transaction dependencies. The paper proposes a novel cross-chain state mapping method with rollback rules, as well as customized block structures and verification algorithms, to address the aforementioned issues. Proof-of-concept experiments are conducted to demonstrate the feasibility of the proposed framework.
Early Software Defects Density Prediction: Training the ISBSG Cross Projects Data using Supervised Learning

Recent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package during the coding phase, and thereby avoid external failure costs. The main goal of the study is to perform an empirical study on early defect prediction at the beginning of a project at the product level of granularity for using it as input in planning quality activities of the project. Hence, both internal and external failure costs could be avoided as much as possible through proper planning of quality. We first made a systematic mapping study (SMS) on secondary studies (literature reviews) on defect prediction to identify the most used datasets, the project attributes and metrics utilized as estimators, and the supervised learning methods employed for training the data. Then, we made an empirical study on defect density prediction using cross-project data. We collected 760 project data from the International Software Benchmarking (ISBSG) dataset version 11, which reported both defects and functional size attributes. We trained the prediction models using: i) the complete set of project attributes, ii) the individual attributes, and iii) multiple subsets of attributes. We employed classification and regression approaches of machine learning. The machine learning models are trained using original values of the dataset, and z-score and logged transformations of original values to explore the effects of data normalization on prediction. Most machine learning models trained on the z-score transformation of the dataset performed best for classifying defects. The Multilayer-Perceptron (Neural Network) model trained on the z-score transformation of complete dataset predicted defects with the highest F1-score of 0.89 using binary classification. The logged transformation and feature selection methods improved the results for multivariable regression. The multivariable regression predicted defects with the highest Root Mean Squared Error (RMSE) and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (r-squared) values of 0.4 and 0.9, respectively, with a subset of 11 features using logged transformation. The results of classification and regression approaches indicate that defects can be predicted with reasonable accuracy at the software product level using cross-project data.
An Application of Evolutionary Neural Networks for Mobile Robots Navigation and Dynamic Obstacles Avoidance

This paper explores an application based on evolutionary computation, in order to train a neural network for reactive navigation. The objective of the mobile robots navigation task is to reach an arbitrary destination point, without colliding with static or dynamic obstacles. The considered environment is an autonomous factory, where multiple robots (all with the same control system - the proposed neural network trained by evolutionary procedures) navigate to transfer parts between processing stations. This environment is considered to be stochastic and dynamic, because robot’s paths between workstations are intertwined. The neural networks training was carried out in an evolutionary way using PSO (particle swarm optimization) algorithm in a series of simulation training maps, in order to maximize the ability to cope with different situations and complexities of environments. Finally, the validation was performed on a simulated environment (map) similar to a real factory. The obtained neural network was able to successfully navigate several robots, without collisions, always reaching the target positions.
The Effectiveness of Electronic Marketing for the Ministry of Tourism in the Kingdom of Saudi Arabia to Achieve Vision 2030 - An Analytical Study on the X Corp “Twitter” Application in the Period from January - December 2023 AD

The importance of this research returns to identifying the effectiveness of e-marketing for the Ministry of Tourism in the Kingdom of Saudi Arabia to achieve Vision 2030. The aim of the research was to analyze the tweets of the MOT’s Twitter account to identify the effectiveness of e-marketing. The researchers have analyzed. The content of the tweets on the period from January to December 2022. The content analysis method was used to analyze tweets about e-marketing in the Ministry of Tourism to provide answers to the research questions, and the intended sample was used as it is most appropriate to conduct this research. The sample size was 113 tweets from the group of 367 tweets that formed the research community. The sample was extracted using the sample equation, and the analysis form was designed in the traditional way, where it was divided into what was said? and how was it said?, The researchers used the SPSS program for statistical analysis and came up with the following results: The official source of the tweets is the Ministry of Tourism's Twitter account, and most of the tweets were in the form of simple news and focused on the tourism sector in all regions of the Kingdom, especially cultural tourism, using promotional posters.
Artificial intelligence in drones and robots for war purposes: a biolegal problem

The ever-increasing use of drones as war weapons is not a concern that is simply left to the news; it is a problem that involves the entire humanity, especially as an aspect that needs to be studied rigorously from biolaw. Among the nuances that this issue entails, the use of artificial intelligence is perhaps the one that, nowadays, receives the most attention, due to the idea of providing a significant level of autonomy in the selection of targets, and the same moving ability that they can reach. to have. This article aims to contribute to this debate, by reviewing artificial intelligence from a bioethical approach, in relation to the persistence of the responsibility that a human being has as a drone operator in a war context.
The role of the mid-level bureaucracy in the COVID-19 pandemic

This article explores the effects of the COVID-19 pandemic on the performance of mid-level bureaucracy. This qualitative and exploratory investigation adopted the research strategy of a case study of the bureaucracy responsible for the social assistance policy of the city of Belo Horizonte, Brazil, in 2020. The research was conducted through bibliographic and documentary research and semi-structured interviews and questionnaires. The bureaucracy’s perception of the changes in the intra-organizational dimensions of public administration caused by the pandemic was captured. It was possible to notice that the crisis affected the work system, the decisionmaking processes, the interactions with other actors, and the skills required for the performance of the functions of these government actors. The research showed that how the mid-level bureaucracy acted helps to understand the functioning of the service and the resilience of public policy in the context of crisis.
Distance Metrics for Machine Learning and it's Relation with Other Distances

In machine learning, distance metrics play a crucial role in measuring the degree of dissimilarity among data points. When creating and optimizing machine learning models, data scientists and machine learning practitioners can make more informed choices by understanding the features of popular distance metrics and their relationships. The effectiveness and interpretability of the model's output can be greatly influenced by selecting the appropriate distance metric. We explain distance metrics and their relevance in machine learning with various examples of metrics, including Minkowski distance, Manhattan distance, Max Metric for R^n, Taxicab distance, Relative distance, and Hamming distance.
PROBLEMS OF FORMING THE ENVIRONMENTAL AWARENESS OF THE PERSONALITY OF THE FUTURE SCIENCE TEACHER

The article reveals modern problems of the formation of environmental awareness of the personality of the future teacher of natural sciences. The features of the future teacher's environmental awareness are determined. Forming the personality of a future
A study on content validity of TEM4 listening comprehension (2016-2023)

Test for English Majors-Band 4 (TEM4) is a large-scale nationwide criterion-referenced test for English majors in Chinese colleges and universities. Since its implementation in 1990, there have been continuous studies on its validity. However, most focused on its reading comprehension section and were based on the traditional classification validity view. Moreover, TEM4 was officially reformed in 2016, especially its listening comprehension section, but relevant validity study is still limited. Therefore, this study aims to assess the content validity of TEM4 listening comprehension after the reform, attempting to enrich the relevant validity study, helping test developers improve question types and promoting the pedagogical teaching to strengthen college students’ listening comprehension ability. Based on Bachman and Palmer’s framework of task characteristics and reference to the interlocutor characteristics proposed by Weir, this study established its framework and analyzed quantitative statistics on the linguistic characteristics of TEM4 listening comprehension. Three dimensions are included in the verification framework: language input characteristics, expected response characteristics, and the relationship between input and response. The study found that the selected TEM4 listening comprehension tests meet the requirements of the teaching and testing syllabuses in terms of length, vocabulary, topic, speech speed, and accent; the question types and expected skills are varied with prominent emphasis; the range and scope of relationship between input and response align with the test proposition principle. All the research results demonstrated a high content validity of TEM4 listening comprehension since its reform in 2016.
A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology

Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.
Slurs in quarantine

We investigate experimentally whether the perceived offensiveness of slurs survives when they are reported, by comparing Italian slurs and insults in base utterances (Y is an S), direct speech (X said: “Y is an S”), mixed quotation (X said that Y is “an S”), and indirect speech (X said that Y is an S). For all strategies, reporting decreases the perceived offensiveness without removing it. For slurs, but not insults, indirect speech is perceived as more offensive than direct speech . Our hypothesis is that, because slurs constitute hate speech, speakers employ quotation marks to signal their dissociation from slur use.
A Diaspora of Humans to Technology: VEDA Net for Sentiments and their Technical Analysis

Background: Human sentiments are the representation of one’s soul. Visual media has emerged as one of the most potent instruments for communicating thoughts and feelings in today's world. The area of visible emotion analysis is abstract due to the considerable amount of bias in the human cognitive process. Machines need to apprehend better and segment these for future AI advancements. A broad range of prior research has investigated only the emotion class identifier part of the whole process. In this work, we focus on proposing a better architecture to assess an emotion identifier and finding a better strategy to extract and process an input image for the architecture.&#x0D; Objective: We investigate the subject of visual emotion detection and analysis using a connected Dense Blocked Network to propose an architecture VEDANet. We show that the proposed architecture performed extremely effectively across different datasets.&#x0D; Method: Using CNN based pre-trained architectures, we would like to highlight the spatial hierarchies of visual features. Because the image's spatial regions communicate substantial feelings, we utilize a dense block-based model VEDANet that focuses on the image's relevant sentiment-rich regions for effective emotion extraction. This work makes a substantial addition by providing an in-depth investigation of the proposed architecture by carrying out extensive trials on popular benchmark datasets to assess accuracy gains over the comparable state-of-the-art. In terms of emotion detection, the outcomes of the study show that the proposed VED system outperforms the existing ones (accuracy). Further, we explore over the top optimization i.e. OTO layer to achieve higher efficiency.&#x0D; Results: When compared to the recent past research works, the proposed model performs admirably and obtains accuracy of 87.30% on the AffectNet dataset, 92.76% on Google FEC, 95.23% on Yale Dataset, and 97.63% on FER2013 dataset. We successfully merged the model with a face detector to obtain 98.34 percent accuracy on Real-Time live frames, further encouraging real-time applications. In comparison to existing approaches, we achieve real-time performance with a minimum TAT (Turn-around-Time) trade-off by using an appropriate network size and fewer parameters.