Reviewerly/exHarmony huggingface dataset:
Extraction and Visually Driven Analysis of VGI for Understanding People’s Behavior in Relation to Multifaceted Context

Abstract Volunteered Geographic Information in the form of actively and passively generated spatial content offers great potential to study people’s activities, emotional perceptions, and mobility behavior. Realizing this potential requires methods which take into account the specific properties of such data, for example, its heterogeneity, subjectivity, and spatial resolution but also temporal relevance and bias. The aim of the chapter is to show how insights into human behavior can be gained from location-based social media and movement data using visual analysis methods. A conceptual behavioral model is introduced that summarizes people’s reactions under the influence of one or more events. In addition, influencing factors are described using a context model, which makes it possible to analyze visitation and mobility patterns with regard to spatial, temporal, and thematic-attribute changes. Selected generic methods are presented, such as extended time curves and the co-bridge metaphor to perform comparative analysis along time axes. Furthermore, it is shown that emojis can be used as contextual indicants to analyze sentiment and emotions in relation to events and locations. Application-oriented workflows are presented for activity analysis in the field of urban and landscape planning. It is shown how location-based social media can be used to obtain information about landscape objects that are collectively perceived as valuable and worth preserving. The mobility behavior of people is analyzed using the example of multivariate time series from football data. Therefore, topic modeling and pattern analyzes were utilized to identify average positions and area of movements of the football teams.
Using virtual PHET media in the physics lessons for explaining atom structure

The analysis of experiments carried out on revealing expediency using virtual laboratory works on atomic and nuclear physics in PHET medium has been presented. The main possibilities and advantages this medium in comparison of traditional lessons are listed. It has been shown that using such medium allows improve essentially knowledge and perception of the real picture of atomic structure by pupils.
Protecting Privacy in Volunteered Geographic Information Processing

Abstract Social media data is used for analytics, e.g., in science, authorities, or the industry. Privacy is often considered a secondary problem. However, protecting the privacy of social media users is demanded by laws and ethics. In order to prevent subsequent abuse, theft, or public exposure of collected datasets, privacy-aware data processing is crucial. In this chapter, we show a set of concepts to process social media data with social media user’s privacy in mind. We present a data storage concept based on the cardinality estimator HyperLogLog to store social media data, so that it is not possible to extract individual items from it, but only to estimate the cardinality of items within a certain set, plus running set operations over multiple sets to extend analytical ranges. Applying this method requires to define the scope of the result before even gathering the data. This prevents the data from being misused for other purposes at a later point in time and thus follows the privacy by design principles. We further show methods to increase privacy through the implementation of abstraction layers. As another additional instrument, we introduce a method to implement filter lists on the incoming data stream. A conclusive case study demonstrates our methods to be protected against adversarial actors.
WorldKG: World-Scale Completion of Geographic Information

Abstract Knowledge graphs provide standardized machine-readable representations of real-world entities and their relations. However, the coverage of geographic entities in popular general-purpose knowledge graphs, such as Wikidata and DBpedia, is limited. An essential source of the openly available information regarding geographic entities is OpenStreetMap (OSM). In contrast to knowledge graphs, OSM lacks a clear semantic representation of the rich geographic information it contains. The generation of semantic representations of OSM entities and their interlinking with knowledge graphs are inherently challenging due to OSM’s large, heterogeneous, ambiguous, and flat schema and annotation sparsity. This chapter discusses recent knowledge graph completion methods for geographic data, comprising entity linking and schema inference for geographic entities, to provide semantic geographic information in knowledge graphs. Furthermore, we present the WorldKG knowledge graph, lifting OSM entities into a semantic representation.
Virtual testing ground for the development of control systems for unmanned vehicles in agriculture

The article describes the technology for deploying and programmatically setting up a virtual test environment for testing algorithms for ground-based agricultural unmanned vehicles. The fields2cover library is used to plan the path, and the Webots simulator integrated with the robot operating system is used as the basis for the test suite. The main task to be solved is to match the coordinate system of the path through the field and the coordinate system of the virtual autonomous vehicle. It also describes the process of creating digital maps using OpenSrtreetMap, converting them into the necessary formats for generating a route in the fields2cover library, and setting up a simulator based on Webots. The work is relevant for educational purposes and is of interest to developers involved in the design of autonomous vehicles.
Development of the principles of parallelization of the symmetric AES encryption algorithm

This article delves into the potential of modern computational systems and technological advancements in refining encryption algorithms, with a particular focus on the Advanced Encryption Standard. The paper underscores the significance of understanding the intricate mechanics and primary processing stages of AES to harness its full potential for parallelization and acceleration. The research emphasizes the pivotal role of Graphics Processing Units in this optimization journey. Given their multi-threaded architecture, GPUs, when integrated with state-of-the-art parallel computing platforms like CUDA and OpenCL, can process vast data volumes with heightened efficiency. This integration not only speeds up the encryption process but also enhances its robustness. The article concludes with a forward-looking perspective, anticipating a deeper convergence between the realms of cryptography and parallel computing. Such a fusion is projected to usher in revolutionary enhancements in both the security and performance facets of encryption systems.
Formation of environmental research competencies of foreign pre-masters’ students for sustainable region development

The article considers the development of pre-master’s programs for foreign citizens in Russian universities. The main content of the article concerns the formation of environmental consciousness of future research engineers. The article analyzes the content of university websites, as well as the publication activity of researchers in the field of training foreign pre-master’s students. The authors highlight universities in which the environmental component of modern engineering master’s programs for foreign citizens is the strongest aspect of the curriculum. The article shows that in these universities pre-master’s training aims primarily at developing environmental research competencies among engineering students. At the same time, the authors pay special attention to activities and tools that contribute to the formation of these competencies, focused on the sustainable development of the regions of Asia, Africa and Latin America. The authors demonstrate their own statistical data on the development of publications of participants in pre-master’s training at Peter the Great St. Petersburg Polytechnic University.
Solvable neural network model for input-output associations: Optimal recall at the onset of chaos

In neural information processing, inputs modulate neural dynamics to generate desired outputs. To unravel the dynamics and underlying neural connectivity enabling such input-output association, we propose an exactly solvable neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs. An analytic form of the response under the input is derived, while three distinctive types of responses including chaotic dynamics are obtained as distinctive bifurcations against input strength, depending on the neural sensitivity and number of inputs. Optimal performance is achieved at the onset of chaos. The relevance of the results to cognitive dynamics is discussed.
MALL‐based collaborative writing in second language classroom: A systematic review

Abstract Driven by the affordances and benefits of mobile technology, collaborative writing (CW) supported by mobile technologies has been increasingly implemented in the second language (L2) acquisition context. To shed light on the research foci and provide guidance for future research in this promising area, the present study conducted an in‐depth and systematic review of 25 empirical articles on CW supported by mobile technologies in the L2 context published from 2014 to 2022, including quantitative, qualitative, and mix‐methods research. A holistic analysis of these articles was conducted, covering various aspects such as research themes, research contexts and technology, theoretical/pedagogical frameworks, writing tasks, and instruments. Based on the analysis of the previous research, this study provided pedagogical suggestions concerning writing tasks, group division, participant training, and writing assessment and also explores future research directions.
Information technologies in teaching English at ecological and environmental protection faculties

Currently, the methods and ways of teaching foreign (English) language in information departments have changed dramatically. A specialist who has received a diploma of higher education must have knowledge of his field and profession, as well as excellent language. A future hacker, an IT technician must speak not only English, but also have the skills and knowledge, as well as the necessary set of terminology in a foreign language in his professional field, have professional communication skills. The use of modern Internet and information technologies in practical classes in the discipline of a foreign language creates conditions for the development of students’ cognitive, educational and language skills. Through information technology, information students improve their writing, speaking, and listening skills. In the presented article, educational Internet materials were considered. It was found that the use of modern information technology in the practical classes significantly increases the motivation of students in the study of a foreign language.
Psychological and environmental workplace well-being’s structural model of the university scientific and pedagogical staff

The article presents the results of the university scientific and pedagogical staff’s survey in the South of Russia (Stavropol Krai, Karachay-Cherkess Republic, and the Republic of Dagestan); it characterizes the level of the workplace environmental well-being. The theoretical model of workplace well-being is developed on the basis of the methodology for assessing psychological and environmental (PE) workplace well-being and updated taking into account modern transformations in the professional activities of scientific and pedagogical staff in the higher education system of Russia. Factor analysis of the results of the survey of 377 persons allowed us to empirically present a structural model that determines the importance of individual factors of PE workplace well-being and substantiate the directions of strategic development in the field of personnel policy of a modern university. The information and analytical base of the research and the current model of PE workplace well-being can be used to develop support programs for different categories of scientific and pedagogical staff of the university. The presented research strategy and methodology for studying the PE workplace well-being of scientific and pedagogical staff makes a theoretical contribution to the improvement of personnel work in the higher education system of Russia.
Topo-heuristic reconfiguration of algebraic LSTM components to optimize temporal modulation in long-range dependencies

In this paper, the authors present a modified LambdaRank ranking algorithm based on the mathematical apparatus of the basic machine learning model (LSTM). LambdaRank is an effective method for ranking objects according to their importance, so it is often used as a mandatory component in search engines and recommendation systems. In this paper, an improvement of the algorithm is proposed by using optimisation techniques and introducing additional parameters for more accurate and stable ranking. The effectiveness of the proposed approach is verified on experimental real application data. The obtained accuracy results of the upgraded algorithm have also been analysed and compared with the classical variation of LambdaRank.
Application of a scientific method to the calculation of environmental and economic damage from a brick factory

The brick factory is located in the village of Gubskaya, Krasnodar Krai. It is engaged in the production of bricks and has a certain impact on the environment. The purpose of the work is to assess the impact on the environment. The location was analyzed, positive and negative sides were noted. The production process is described, the natural and climatic characteristics are analyzed. Environmental and economic damage is calculated. The calculation of the amount of prevented environmental and economic damage from polluted water bodies in the village of Gubskaya, Krasnodar Krai was made.
The current state of crypto-market in the USA: regulation and future prospects in the context of the need to develop energy efficiency and achieve the Sustainable Development Goals

The paper traces the evolution of regulation of the crypto market in the United States in the context of the need to increase energy efficiency and achieve the Sustainable Development Goals. We analyze two levels of regulation, including federal regulations and state laws, that are changing to meet new environmental health pressures. There appears to be a lack of consensus at both of these levels, whether due to (significant) interstate differences or disagreements between various federal agencies and departments. We classify US states into four groups based on the nature of their cryptocurrency legislation and describe states that do not fall into this classification. We also explore the properties and features of the digital dollar concept and analyze the prospects for its development in products of the near future.
Workflow automation and performance improvement based on PostgreSQL

This article discusses the development of an automated information system for improving and improving the efficiency of the cinema. This is achieved by automating the process of submitting requests, monitoring the quality and quantity of solutions for such requests. The system is designed to provide access to the list of services provided, its timely updating and optimization; the formation of all types of reports; providing managers with a tool that automates most of the routine work on the registration of the results of the cinema.
The Synergy of Educational Technology Advancement and Human Capabilities in the Era of Society 5.0

In the field of education, the synergy between the industrial revolution 4.0 and society 5.0 gives a new perspective in the learning process. Education nowadays transcends location and time and may take place without the need for a classroom. Education is becoming more open and accessible, which implies that technological improvements might make it easier for us to carry out the learning process. Furthermore, a pandemic has just evolved, which not only has a detrimental influence but also delivers advantages. The presence of a pandemic in learning necessitates the ability of academics and learners to respond to these changes. By utilizing technological advances in learning, the industrial revolution 4.0 and society 5.0, as well as the pandemic, developed a synergy that increases individuals’ ability to adapt fast to changes that occur. Rapid technology advancements must be compatible with the skills and adaptability of individuals who operationalize technical outcomes. This research shows that technology is not a tool that can replace humans in their roles, but the existence of more advanced technology may make human life easier, particularly in the field of education, so that education can be more adaptive to the times and more imaginative in planning its demands. Society 5.0 is a period in which technology is integrated with human beings. As a result, developments in information technology must be matched with a supporting educational curriculum in order to produce people with soft skills that can compete globally
Cluster as the basis for creating an innovative educational product

The article reveals the basics of the strategy of modernization of the education system. At the present stage of its reform, they are aimed at increasing the competitiveness of educational institutions in the educational services market, at creating maximum accessibility and openness of education for the population and business. They should be carried out within the framework of innovations, taking into account the main directions of the socio-economic development of the republic, the implementation of priority areas of state policy in the field of education.
Bilingual short texts incorporating emojis sentiment calculation

The text posted by users often consists of a mixture of Chinese, English, and emojis, rather than a single language corpus. However, traditional sentiment analysis models have not effectively solved the problem of sentiment calculation in bilingual text with emojis, or even the problem of sentiment fluctuations between different modules. In response to these issues, this paper proposes a centrifugal fusion method to accomplish sentiment fusion of mixed-language phrases, and a centrifugal factor to address sentiment fluctuation in bilingual short text incorporating emojis. The bilingual sentiment analysis experiment, incorporating emojis into the Weibo dataset, yielded promising results.
Evaluating the Performance of Mobile-Convolutional Neural Networks for Spatial and Temporal Human Action Recognition Analysis

Human action recognition is a computer vision task that identifies how a person or a group acts on a video sequence. Various methods that rely on deep-learning techniques, such as two- or three-dimensional convolutional neural networks (2D-CNNs, 3D-CNNs), recurrent neural networks (RNNs), and vision transformers (ViT), have been proposed to address this problem over the years. Motivated by the fact that most of the used CNNs in human action recognition present high complexity, and the necessity of implementations on mobile platforms that are characterized by restricted computational resources, in this article, we conduct an extensive evaluation protocol over the performance metrics of five lightweight architectures. In particular, we examine how these mobile-oriented CNNs (viz., ShuffleNet-v2, EfficientNet-b0, MobileNet-v3, and GhostNet) execute in spatial analysis compared to a recent tiny ViT, namely EVA-02-Ti, and a higher computational model, ResNet-50. Our models, previously trained on ImageNet and BU101, are measured for their classification accuracy on HMDB51, UCF101, and six classes of the NTU dataset. The average and max scores, as well as the voting approaches, are generated through three and fifteen RGB frames of each video, while two different rates for the dropout layers were assessed during the training. Last, a temporal analysis via multiple types of RNNs that employ features extracted by the trained networks is examined. Our results reveal that EfficientNet-b0 and EVA-02-Ti surpass the other mobile-CNNs, achieving comparable or superior performance to ResNet-50.
Robust quantum teleportation via a non-Markovian channel

In this paper we propose a non-Markovian quantum channel approach to mitigating the degradation of the average fidelity in continuous-variable quantum teleportation. The non-Markovian quantum channel is modeled by an augmented system where ancillary systems are introduced to represent the internal modes of non-Markovian environments. With a proper non-Markovianity, enhanced effects of the channels on continuous-variable quantum teleportation are observed. Also, the logarithmic negativity of entangled states can be effectively maintained so that the decay of the average fidelity of quantum teleportation is mitigated. In addition, the analysis on different teleported quantum states shows that coherent states exhibit stronger robustness than those of squeezed states and cat states in quantum teleportation with a non-Markovian channel.
Contrastive representation learning with noisy pseudo labels for unsupervised person re-identification

Currently, state-of-the-art unsupervised person re-identification(Re-ID) algorithms employ cluster methods to create pseudo labels by grouping unlabeled data to train the model, but it inevitably generates noisy pseudo labels, which limits the performance of unsupervised person Re-ID task. To tackle the above issue, we suggest the contrastive regularization loss function for the model to concentrate on learning the representation information of correct pseudo labels and ignore those of noisy pseudo labels to the maximum extent. Under the guidance of this method, the model training focuses more on the learning of high quality correct pseudo labels and eliminates the negative consequences of noisy pseudo labels on it, thus boosting the efficiency of the unsupervised person Re-ID mission. The proposal is adequately experimented on three person Re-ID benchmark datasets, and the results prove the usefulness of the method and outperform other state-of-the-art approaches.
A multibeam outlier detection algorithm based on density clustering

The multibeam echo sounder (MBES) is widely used in oceanic exploration for various purposes such as submarine topographic surveying, geological investigations, and salvage operations for submarine wrecks. The multibeam data is prone to measurement errors due to various factors such as the intricate acoustic environment in the ocean, motion sensor errors, and sonar echo multipath interference. The task of automatically cleaning MBES datasets remains a challenging one. This study proposes an automatic cleansing strategy for MBES datasets based on the density clustering method to identify and reject anomalous data by continuous clustering of actual topographic data. The analysis initially focuses on the characteristics of various types of multibeam data outliers. Then, the study proposes an improved OPTICS method for detecting outliers in multibeam data. The RD computed through OPTICS serves to detect isolated outliers, while topological analysis is employed to identify structural outliers. Finally, the proposed algorithm is tested on MBES data collected by iBeam 8140 sonar sensor.
A Dynamic Parameter Adaptive Path Planning Algorithm

Path planning in complex environments has always been a focus of research for scholars both domestically and internationally. This study addresses the challenge of path planning that combines obstacle avoidance and optimal path searching in scenarios lacking prior knowledge. The proposed approach introduces a parameter dynamic adaptation strategy for path planning. Experimental investigations are conducted using grid-based maps, and the results demonstrate that the method presented in this paper surpasses Q-learning and Sarsa algorithms in terms of comprehensive exploration, enhanced stability, and quicker convergence speed.