Reviewerly/exHarmony huggingface dataset:
Astrolabe: Visual Graph Database Queries with Tabular Output

Graph databases are an established solution for large, highly connected datasets. One challenge associated with deploying graph databases in industrial settings is usability. Typically, developers interact with graph databases through queries in languages such as Cypher or GraphQL. Many end-users, analysts, and administrators are not familiar with these specialized languages. Additionally, these queries return hierarchical data in formats such as JSON (JavaScript Object Notation) or XML (Extensible Markup Language). Additional scripts and interfaces are needed to convert hierarchical data into more easily digested tables. To overcome these challenges, each graph database use-case typically involves significant custom software to explore, view, and export data.
MSRA: A Multi-Aspect Semantic Relevance Approach for E-Commerce via Multimodal Pre-Training

To enhance the effectiveness of matching user requests with millions of online products, practitioners invest significant efforts in developing semantic relevance models on large-scale e-commerce platforms. Generally, such semantic relevance models are formulated as text-matching approaches, which measure the relevance between users' search queries and the titles of candidate items (i.e., products). However, we argue that conventional relevance methods may lead to sub-optimal performance due to the limited information provided by the titles of candidate items. To alleviate this issue, we suggest incorporating additional information about candidate items from multiple aspects, including their attributes and images. This could supplement the information that may not be fully provided by titles alone. To this end, we propose a multi-aspect semantic relevance model that takes into account the match between search queries and the title, attribute and image information of items simultaneously. The model is further enhanced through pre-training using several well-designed self-supervised and weakly-supervised tasks. Furthermore, the proposed model is fine-tuned using annotated data and distilled into a representation-based architecture for efficient online deployment. Experimental results show the proposed approach significantly improves relevance and leads to considerable enhancements in business metrics.
Priv-S: Privacy-Sensitive Data Identification in Online Social Networks

Privacy inference imposes a serious threat to user privacy in Online Social Networks (OSNs) as the vast amount of personal data and relationships in OSNs can be used not only to infer user privacy but also to enrich the training set of inference methods. Previous studies have mostly focused on privacy inference from the perspective of the adversary with the objective of tracking the accuracy of the inference results. However, countering privacy inference requires not only the analysis of the inference method but also the roles that data would play in the inference process, i.e., to determine the sensitivity of the data. To address this issue, in this paper, we propose a model for the identification of privacy-sensitive data in which we formulate the identification as an influence maximization problem to identify both the privacy-sensitive users and the privacy-sensitive attributes for privacy inference. In our model, a privacy-affected tree is first constructed based on the influence between users with respect to the concerned privacy. Then, privacy-sensitive users in the privacy-affected tree are identified along with their privacy-sensitive attributes based on their contribution to the inference result of the concerned privacy. Experiments show that the results of our proposed model can significantly affect the accuracy of privacy inference, which demonstrates that our model can identify the privacy-sensitive data. Meanwhile, the impact of privacy-sensitive users and privacy-sensitive attributes is analyzed to guide the design of effective privacy-enhancing technologies.
Personal reflection and responsibility in the conditions of continuous social changes

The article reveals the peculiarities of personal reflection and responsibility in the conditions of constant social changes. It is shown that the evolution of socially significant knowledge involves a change in approaches to its reproduction, in particular, new content is extracted from previously existing, philosophically grounded social knowledge and meanings, which directly relates to the process of reflection and development of responsibility. Social psychology is currently stimulated by epistemology to carry out interdisciplinary analytical generalizations, which is also reflected in modern theoretical constructions. Consistency and integration of two spheres of knowledge - sensibility and rationality, are able to transform individual fragments of acquired knowledge into a system of world understanding, forming a synthetic perception that determines world understanding. At the same time, the imagination, capable of providing detailed knowledge about the subject, also builds a corridor of possibilities for the development of reflection and personal responsibility in the conditions of constant social changes. Keywords: personal reflection, responsibility, social responsibility, social changes.
A Case Study of Learners' Cognitive Load in Mathematics Problem Solving Process

This study observed the case of cognitive load of learners solving mathematics problems by mixing physiopsychological and subjective measurement methods, and empirically explored the cause of increasing cognitive load. Specifically, it was intended to confirm the actual pattern of cognitive load of learners according to the content area and element. Accordingly, the results of the cognitive load analysis of learners who solve mathematics problem were presented and the factors that caused cognitive load in each content area and problem design were derived. Afterwards, by summarizing the analysis and its characteristics, we discussed the implications for mathematics problem solving teaching and learning design and improvement directions.
A Comparative Analysis of Global Central Bank Digital Currencies

The current study aims to describe and analyse the digital currencies issued by 11 countries across six continents. The paper also aims to create a base for the initiation of a digital currency. With cryptocurrency posing certain threats, Central Bank Digital Currency (CBDC) may emerge as a safe alternative. The Atlantic Council report states that nearly one hundred countries in the world are exploring the possibility of introducing CBDCs and at the same time, 11 countries have fully launched CBDCs. In this context, the present study takes a comparative approach, and focuses on the need and rationale for CBDCs as stated by different countries, their implementation mechanisms, current status, adoption rate, initiatives taken to create awareness among citizens, and emerging lessons. The study is descriptive in nature and is based on secondary data drawn from publications by the Central Banks of the countries, the International Monetary Fund (IMF), the World Bank, the Atlantic Council CBDC tracker, Research Papers and newspapers /online websites, etc.
THE MAIN ASPECTS OF THE PERCEPTION OF THE HISTORICAL EVENT OF THE "DECEMBRIST UPRISING" THROUGH THE PRISM OF RUSSIAN CINEMA

The article discusses the main tools of influencing the viewer used in cinematography, as well as methods and methods of conveying the author's view on the interpretation of historical events and personalities. In particular, an example of the viewer's perception of such a landmark in the history of Russia as "The Decembrist Uprising" is given on the example of two domestic films: "The Star of Captivating Happiness" and "Union of Salvation". The authors of the study aim to analyze the cinematic ways of influencing the viewer in both films, as well as to compare the point of view of the filmmakers on the historical events being filmed.
Phish2vec: A Temporal and Heterogeneous Network Embedding Approach for Detecting Phishing Scams on Ethereum

The exponential growth of Ethereum transactions has resulted in a significant increase in phishing scams, leading to substantial financial losses in recent years. Current machine/deep learning-based approaches for classification have been found to be inadequate for large-scale and label-imbalanced Ethereum scenarios. To address this issue, we propose Phish2vec, a novel network embedding approach that takes into account the transaction temporality and heterogeneity in detecting phishing scams on Ethereum. Our approach begins by producing a transaction sub-network through data collection and preprocessing, which includes a novel Statistics-Based Sampling (SBS) method to address label leakage. To generate sequences that contain more comprehensive information, we then utilize two different types of sequences generators: Temporal-based Sequences Generator (TSG) and Heterogeneous-based Sequences Generator (HSG). By concatenating the sequences generated by TSG and HSG together, and feeding them into Word2vec and Fully Connected neural network (FC), our approach can identify phishing accounts with an Fl-score as high as 82.05%, which significantly outperforms classic schemes such as DeepWalk (67.29%), Trans2vec (74.78%), and Node2vec (70.91%).
Digital technologies in the professional training of masters in the direction of «physical culture and sports»

Relevance. The article presents the results of a survey of the teaching staff and undergraduates of higher educational institutions on the problem of distance learning. The attitude of undergraduates to various forms of the educational process, to the digitalization of learning, to innovative technologies is revealed. The experience of introducing innovative and digital learning technologies based on a competence-based approach is proposed. The purpose of the study is a scientific and theoretical substantiation of the scientific and pedagogical foundations of distance learning and confirmation of their effectiveness through pedagogical experimentation. Methods and organization of research. We conducted a survey among teaching staff and students of 1-2 years of study. Students from Kyrgyzstan were covered (Kyrgyz State Academy of Physical Culture and Sports, S. Naamatov National University, Osh State Pedagogical University, International Medical University, Talas State University, Kyrgyz-Turkish University "Manas", Kyrgyz National, KEU, KNU. Zhusupa Balasagyna, International University "Ala-Too", IAUPFiB), Kazakhstan (Kazakh Academy of Tourism and Sports, International University named after K.Sh.Toktomamatova, Auezo-va UCU), Russia (FGBOU VO PGUFKSiT, GAOU PO Moscow City Pedagogical University, Volga Region University of Physical Culture, Sports and Tourism, as well as the Accreditation Agency "Bilim Standard". A total of 258 teachers took part in the survey, 38 % of them men, 62 % women: 2 % are doctors of sciences, 35.7 % are candidates of sciences and associate professors, 60.5 % are teachers without a degree. The survey was attended by teachers of the Faculty of Physical Culture and Sports, as well as natural geography, Engineering, History, Physics and Mathematics, Economics, Law, Philology faculty. Conclusions. The history of the formation of the educational process and the realities of the current time give reason to express the opinion that there is no limit to the improvement of the learning process and the technology of teaching.
Three Common Swedish Verbs and Examples of their Translation into English, German, Latvian and Russian

Ungārijas un Vācijas
Process automatisation of Xcode new target creation

We live in the era of the Internet and technology. Now it is important for a person to be able to do their routine, uninteresting things quickly, and to be able to focus on more professional things. Everyone wants to minimize the time spent searching for information and have all services in one place. Due to these desires, significant progress is being made in the development of mobile applications. A number of factors also contribute to this. First, the development of the hardware of pocket-sized devices reaches a level sufficient for complex computing tasks. Secondly, the speed of Internet communication has increased. Third, almost everyone in the world now has a smartphone, so it is much more profitable for businesses to develop mobile applications to reach a wider audience. The development of applications for mobile devices is becoming more and more popular, but at the same time more expensive. In some cases, a business needs to have a number of similar applications for one reason or another. There are several approaches to code reuse in the context of iOS development. One is to use a common codebase and separate Xcode targets for each product. The process of creating a new target requires a number of routine operations, which require the programmer’s time, and writing code is potentially error-prone. This research proposes to improve this approach by using scripts to generate a project file based on given configurations. A console script for generating a project file and a project to illustrate the approach have been developed.
Performacnes comparison of CNN-based models for fine-grained image classification

Fine-grained visual classification is regarded as a more refined classification that can identify specific types of objects. It has been widely used in commodity sales, vehicle recognition and person recognition, etc., and has played a great value in many fields. For example, it requires an algorithm to identify different species of birds or dogs to facilitate more practical applications. This task is difficult since the objects has similar appearance and there exist obvious intra-class variance and limited inter-class differences, where different kinds of birds could share very similar appearance. Deep learning techniques has been applied to image recognition, natural language processing, and many other fields. Several approaches tackling the fine-grained classification problem are proposed. To further demonstrate the different designs of these solutions, in this paper, fine-grained identification methods are compared and analyzed, among which WS-DAN achieves better results and it is preferred to be an effective method, which is expected to be more widely used in this field.
Prediction and feature importance analysis for wordle strategy based on machine learning models

The word guessing game, Wordle, has attracted considerable attention from researchers in computer studies and mathematics due to its significant relevance. Many studies have been taken on Wordle to explore its potential in simulating human behaviors during gameplay and devising optimal strategies for players. In this paper, strategies based on mathematics theories or machine learning technologies are compared by their feature importance in order to explore whether there is a loss of feature meaning in machine learning methods. In the whole study, the effect of features and their relations to the expectations of tries of game Wordle are also explored. More specifically, this paper introduces machine learning models such as linear, random forest, bagging as well as gradient boosting decision trees to visually provide a relation between words features (such as repeat frequency, vowels, consonants) and the expectation of attempts in a game. After establishing machine learning models, the feature importance is derived by feature engineering techniques. The importance is then compared with spearman statistical correlations based on the dataset to hence draw the conclusion of the change of feature meaning in machine learning methods. Study results indicate that there is loss of meaning and effect of features in the better fitted prediction models (gradient boosting decision trees, random forest) compared to the statistic approaches.
WEB BASED E VOTING TRAINING FOR STUDENT COUNCIL CHAIRMAN ELECTION AT HIGH SCHOOL

Implementation in the process of electing the student council president involves information technology using the electoral system as learning for students who practice democracy directly. Activities, especially the election of the Student Council chairperson, still use media assisted by simple information, namely in the form of sheets of paper, and are still not effective. The problem faced requires a new motivation that is easier to guide information on the election of the Student Council chairperson in Palembang City Senior High School as well as minimizing information fraud in the election and facilitating the election of the Student Council chairperson. This study introduces the E-Voting Website to Student Council Palembang City by bringing up the idea of implementing the Student Council chairman election by utilizing development information.
Coexistence, Tarrying With The Negative

본 연구는 다문화 시대에 ‘공존’을 종교철학의 관점에서 고찰한다. 다문화 시대의 공존은 서로의 차이를 없애는 '동일화의 과정'이 아니다. 다문화 시대의 공존은 ‘동일성의 공존’이 아닌, ‘차이 그 자체’를 인정하는 ‘차이의 공존’이다. 차이의 공존은 이질적인 타자성을 수용하는 ‘부정성과 함께 머물기’이며, 이러한차이의 공존은 기독교 성육신에 나타난 것처럼 타자-되기의 윤리에 나타난다.This study examines 'coexistence' in the multicultural era from the perspective of religious philosophy. Coexistence in a multicultural era is not a 'process of identification' that removes differences from each other. The coexistence of the multicultural era is not the 'coexistence of identity', but the 'coexistence of difference' that recognizes 'the difference itself'. the coexistence of differences is 'tarrying with the negative' that embraces heterogeneous otherness, The coexistence of these differences appears in the ethics of becoming-other, as shown in the Christian incarnation.
Algorithms for Solving Problems of Resources Allocation in the Management of Business Processes in Educational Organizations

This article deals with the problem of optimal resources allocation in reference to the area that is currently relevant in the Republic of Kazakhstan, the educational system and management issues in educational organizations in rapidly changing economic and social conditions. A model for the optimal resources’ allocation in the management of business processes that exist in educational organizations has been developed using the example of one of the key business processes. Such research methods as search, survey, Fishbone diagrams and heuristic methods were used. A computational algorithm was developed and the testing results on the suggested example were presented. Comparative analysis shows that the developed computational algorithm based on the application of the linear programming method results in the optimal resources allocation in the considered business process. The analysis of existing methods reveals their limitations, particularly in dealing with dependent operations. The research findings and approaches have practical implications for improving the management system and enhancing the quality of business processes in educational organizations. The algorithms and models developed in this study can be applied not only to solve load distribution issues among teachers but also to address resource allocation problems in other areas of educational institutions.
Identification of mobile development issues using semantic topic modeling of Stack Overflow posts

Background Increasing demands for mobile apps and services have recently led to an intensification of mobile development activities. With the proliferation of mobile development, there has been a major transformation in the architectures, paradigms, knowledge domains and skills of traditional software systems towards mobile development. Therefore, mobile developers experience a wide spectrum of issues specific to development processes of mobile apps and services. Methods In this article, we conducted a semantic content analysis based on topic modeling using mobile-related questions on Stack Overflow, a popular Q&amp;A site for developers. With the aim of providing an understanding of the issues and challenges faced by mobile developers, we used a semi-automated methodology based on latent Dirichlet allocation (LDA), a probabilistic and generative approach for topic modeling. Results Our findings revealed that mobile developers’ questions focused on 36 topics in six main categories, including “Development”, “UI settings”, “Tools”, “Data Management”, “Multimedia”, and “Mobile APIs”. Besides, we investigated the temporal trends of the discovered issues and their relationships with mobile technologies. Our findings also revealed which issues are the most popular and which issues are the most difficult for mobile development. The methodology and findings of this study have valuable implications for mobile development stakeholders including tool builders, developers, researchers, and educators.
Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion

Structured light technology is typical for capturing 3D point cloud data. This paper proposes a 3D reconstruction system to obtain point cloud data of complex objects based on nine-order Gray code and an eight-step structured light projection combined with a phase shift and phase unwrapping method. In this system, two projectors serve as bilateral projectors for structured light, along with a camera and rotating platforms. These components were used to obtain point cloud data from multiple perspectives, which helps avoid the shadow areas caused by a single projection angle and provides complementary point cloud data. The point clusters scanned under each perspective were transformed into the same coordinate system. Furthermore, a distance-based point cloud noise removal algorithm was proposed to optimize platform noise and facilitate point cloud data fusion. The experimental results proved that the system effectively captures 3D point cloud data for complex objects. The dimensional quantitative analysis of an aero engine blade was also performed.
Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOv7

The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.
Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images

The paper presents a study of various approaches to the classification of soil covers based on neural network algorithms using hyperspectral remote and proximal sensing of the Earth. The spectral distributions were recorded in the laboratory using an Offner imaging scanning hyperspectrometer. Spectral-spatial characteristics of nine soil samples from various parts of a farming land in the Samara region were experimentally studied. Using a method of energy dispersion microanalysis, the correspondence between the hyperspectral data and the chemical composition of the samples taken was established. Based on the data obtained, a neural network-aided classification of soil samples was implemented depending on the content of constituent elements such as carbon and calcium. A normalized spectral-spatial convolutional neural network was used as a classifier. As a result of the work, an approach to the classification of high-resolution hyper-spectral images based on the refinement of a multiclass convolutional neural network using an ensemble of binary classifiers is proposed. It is shown that the classification of soil samples by carbon and calcium content is carried out with an accuracy of 0.96.