Reviewerly/exHarmony huggingface dataset:
Slogans, Pragmatism, and Crisis Response at the Grassroots Level: An Analysis of Anti-Coronavirus Disease 2019 (Anti-COVID-19) Slogans in China

While slogans have been an indispensable propaganda and mobilisation tool in Chinese political life for a long time now, the unprecedented pressure brought by the coronavirus disease 2019 (COVID-19) epidemic made them take on a totally new appearance. What, then, do these slogans look like? How do they work? By conducting a rhetorical analysis of 228 anti-COVID-19 slogans used by grassroots governments in China, this study further finds multiple persuasion strategies embedded in them. On the one hand, at the grassroots level eye-catching language and expressions are used to attract public attention, linking individuals’ behaviour to the interests of their families, as well as those of society and the country as a whole. On the other hand, those who violate government policies are negatively portrayed through a series of authoritative discourses. Behind these persuasion strategies lies a political pragmatism with regard to crisis response, attempting therewith to demonstrate and expand the legitimacy of the party-state. However, some slogans also contradict the Communist Party of China's advocacy of modernising the country's governance system and capacity, even though they may have been useful in slowing down or containing the spread of COVID-19.
Category‐related attention domain adaptation for one‐stage cross‐domain object detection

Abstract Cross‐domain object detection aims to generalize the distribution of features extracted by an object detector from an annotated domain to an unknown and unlabelled domain. Although one‐stage cross‐domain object detectors have significant advantages in deployment than two‐stage ones, they suffer from two problems. First, neglect of category features and inaccurate alignment between multiple category features would lead to decreased domain adaptation efficiency. Second, one‐stage detectors are more sensitive to imbalance of samples and negative samples severely affect the alignment process of domain adaptation. To overcome these two problems, an innovative category‐related attention domain adaptive method that refines discrimination for each category's feature has been proposed in this paper. In the proposed method, a group of domain discriminators is assigned to each category to refine the fine‐grained features between categories. The discriminators are trained via an adversarial discriminant framework to align the fine‐grained distributions cross different domains. A category attention alignment (CAA) module is proposed to navigate more attention to the foreground regions in instance‐level, which effectively alleviates the negative migration problem caused by the positive and negative sample imbalance of the one‐stage detector. Specifically, two sub‐modules in the CAA module are developed: a local CAA module and a global CAA module. These modules aim to optimize the domain offsets in both the local and global dimensions. In addition, a progressive global alignment module is designed to align image‐level features, offering prior knowledge of migration for the CAA module. The progressive global alignment module and CAA module collaboratively engage in benign competition with the backbone network across various levels. Extensive transferring experiments are conducted among cityscapes, foggy cityscapes, SIM10K, and KITTI. Experimental results show that the proposed method has much superior performance than other one‐stage cross‐domain detectors.
Teacher profiles concerning upper secondary school teachers' views on and use of digital learning resources for teaching – a cluster analysis

The aim of this study is to characterize upper secondary school teachers’ views on and use of digital learning resources (DLR) for teaching and explore whether there are differences between teachers’ profiles in relation to demographic aspects. The data was collected through an online survey from a sample of 243 teachers from 23 upper secondary schools in Sweden. The survey included questions on why and how teachers used DLR for teaching. The data was analysed using exploratory factor, cluster, and post hoc analyses. Five different teacher profiles were identified: high, high general, medium, medium general, and low DLR capacity. These profiles differed significantly concerning self-reported skills, use, and purposes of DLR. Moreover, the five profiles also differ significantly on demographic factors such as teacher degree. These findings have important implications for the design of in-service teacher training and pre-service teacher education programs concerning teachers’ skills, didactic purposes, and use of DLR.
Assessing code readability in Python programming courses using eye‐tracking

Abstract Code readability models are typically based on the code's structural and textual features, considering code readability as an objective category. However, readability is inherently subjective and dependent on the knowledge and experience of the reader analyzing the code. This paper assesses the readability of Python code statements commonly used in undergraduate programming courses. Our readability model is based on tracking the reader's eye movement during the while‐read phase. It uses machine learning (ML) techniques and relies on a novel set of features—observational features—that capture how the readers read the code. We experimented by tracking the eye movement of 90 undergraduate students while assessing the readability of 48 Python code snippets. We trained an ML model that predicts readability based on the collected observational data and the code snippet's structural and textual features. In our experiments, the XGBoost classifier trained using observational features exclusively achieved the best results (0.85 F ‐measure). Using correlation analysis, we identified Python statements most affecting readability for undergraduate students and proposed implications for teaching Python programming. In line with findings for Java language, we found that constructs related to the code's size and complexity hurt the code's readability. Numerous comments also hindered readability, potentially due to their association with less readable code. Some Python‐specific statements (list comprehension, lambda function, and dictionary comprehension) harmed code readability, even though they were part of the curriculum. Tracking students' gaze indicated some additional factors, most notably nonlinearity introduced by if, for, while, try, and function call statements.
AGI’s Hierarchical Component Approach to Unsolvable by Direct Statistical Methods Complex Problems

The amazing deep neural network (DNN) advances over the past 10 years have made it possible, if there is enough data and computing power, to achieve solutions to unexpectedly complex problems. But DNN does not explicitly use decomposition, the main advancement method in complicated task solving. The automatic complex scenes decomposition can be carried out based on mapping by a neural network. The problem is the impossibility to map complex objects and phenomena state spaces. The hierarchical complex scene’s division into simple components can be a key for solving the problem. The hierarchically organized structure of simple objects and phenomena maps of different abstraction levels can make it possible to solve problems in a complex environment, in which all properties cannot directly be revealed by statistical methods. Operation modes of such a hierarchical structure can be correlated with terms used in philosophy and psychology.
Object‐aware deep feature extraction for feature matching

Summary Feature extraction is a fundamental step in the feature matching task. A lot of studies are devoted to feature extraction. Recent researches propose to extract features by pre‐trained neural networks, and the output is used for feature matching. However, the quality and the quantity of the features extracted by these methods are difficult to meet the requirements for the practical applications. In this article, we propose a two‐stage object‐aware‐based feature matching method. Specifically, the proposed object‐aware block predicts a weighted feature map through a mask predictor and a prefeature extractor, so that the subsequent feature extractor pays more attention to the key regions by using the weighted feature map. In addition, we introduce a state‐of‐the‐art model estimation algorithm to align image pair as the input of the object‐aware block. Furthermore, our method also employs an advanced outlier removal algorithm to further improve matching quality. Experimental results show that our object‐aware‐based feature matching method improves the performance of feature matching compared with several state‐of‐the‐art methods.
Online harassment of journalists in Nigeria: audience motivations and solutions

This study investigates the motivations for the hostility towards the press by the audience and how to control online harassment of journalists in Nigeria. Data for this study are from online and face-to-face semi-structured interviews of 54 Nigerians in Nigeria. Finding shows that perceived journalistic malpractice and unethical behaviour motivate Nigerians to engage in online harassment of journalists in Nigeria. The study also uncovers what the audience in Nigeria offer as preventive measures to online harassment of journalists. These include (i) improved transparency, (ii) improved ethical conduct by journalists, and (iii) procedural and prosecutorial measures (e.g. implementation of a robust professional code of conduct and enacting safety laws for journalists) as ways of eradicating online harassment of journalists in Nigeria. Suggestions for future research areas were delineated.
Kairos: Exploring a Virtual Botanical Garden through Point Clouds

This paper reports the design, implementation and evaluation of a point cloud-based virtual reality environment for a botanical garden. The objective of this work was the creation of a point cloud-based digital timestamp of the Botanical Garden of the University of Coimbra, capturing reality and thus a moment in time that can be run and visualised in 3D space in real-time. This environment consists of three distinct locations within the Garden, containing century-old tree specimens. These areas were digitised through photogrammetry to create point clouds, which were then used as the basis of an immersive audiovisual experience whose main goal was to convey the ambiance of the Garden, along with showing people its natural processes, usually hidden to the naked eye, with the aim of rekindling our relationship with nature. We produced multimedia content consisting of PC- and VR-based experiences and 360° videos. Both PC and VR variants were evaluated by 22 volunteers where we assessed the user experience, the resulting ambiance and the gameplay. The results show a positive response from users, which described the resulting experience as “Peaceful”, “Calming”, “Relaxing”, “Dreamy”, “Colourful”, “Immersive” and “Nice”.
Explaining pretrained language models' understanding of linguistic structures using construction grammar

Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasizing the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central building blocks of language, i.e., linguistic units of different granularity that combine syntax and semantics. As a first step toward assessing the compatibility of CxG with the syntactic and semantic knowledge demonstrated by state-of-the-art pretrained language models (PLMs), we present an investigation of their capability to classify and understand one of the most commonly studied constructions, the English comparative correlative (CC). We conduct experiments examining the classification accuracy of a syntactic probe on the one hand and the models' behavior in a semantic application task on the other, with BERT, RoBERTa, and DeBERTa as the example PLMs. Our results show that all three investigated PLMs, as well as OPT, are able to recognize the structure of the CC but fail to use its meaning. While human-like performance of PLMs on many NLP tasks has been alleged, this indicates that PLMs still suffer from substantial shortcomings in central domains of linguistic knowledge.
A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images

Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. Materials and methods: We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. Results: A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers. Conclusions: We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.
A coarse- and fine-grained niching-based differential evolution for multimodal optimization problems and its application in multirobot task allocation

Multimodal optimization problems (MOPs) need to locate the global optima as many as possible and refine the accuracy of the identified optima as high as possible. How to divide niches reasonably and search solutions effectively are two difficult problems facing MOPs. In this paper, a coarse- and fine-grained niching-based differential evolution (CFNDE) algorithm with micro-search strategy is proposed to divide population reasonably and search the optima effectively, which mainly includes four contributions. Firstly, a framework of coarse- and fine-grained niching (CFN) strategy is proposed, where the coarse-grained clustering method provides the information of clusters to the fine-grained clustering method to accurately divide the population into niches. Secondly, a micro-search-based mutation (MSM) strategy is proposed to accelerate the convergence of population, which searches for the optimal solution in the range between the current individual and the nearest individual. Thirdly, a multi-level local search (MLLS) strategy that incorporates the cluster-level and population-level search mechanisms is proposed to refine the accuracy of solutions. Finally, a stagnation detection and re-search (SDR) strategy is proposed to re-search the local optima in evolution. We compare CFNDE with the-state-of-art multimodal optimization algorithms on the widely used benchmark CEC’2013. The experimental results show that CFNDE performs better than or similar to some recently proposed algorithms in 17 out of 20 tested functions. Besides, the proposed CFNDE is also applied to the multirobot task allocation problems and CFNDE provides multiple completely different optimal scheduling schemes to deal with emergencies.
INFORMATION TECHNOLOGY FOR REAL-TIME MONITORING AND VISUALIZATION OF LOAD IN URBAN PUBLIC OPEN SPACES BASED ON SPATIAL AND STATISTICAL DATA ANALYSES OF HUMAN BEHAVIOUR

The article deals with the information technology development for monitoring and visualization of the load of urban environment public open spaces and human behaviour. The problem of urban public open space load became actual both in everyday life and in the conditions of pandemics. The offered information technology includes several dynamic or real-time data acquisition modules, data server with data processing, clustering and simulation functions and mobile application for end users on their mobile devices. A mobile application warns on public open space load at a certain level of density, recommends residents to visit territory in another times or offer less congested areas or other nearby public open spaces. The solution will also be able to facilitate the events by controlling and gradually changing the flow of visitors. Mobile application will offer users statistical data analysed from the STATBOX registrations modules placed in the parks. This data includes information on activities of the visitors, the number of the visitors in them in real time, visitors with animals, possibilities of recreation at the territory, park load as well as distance to the park and transportation possibilities. The messaging section will provide the possibility to users to send information about pleasant/unpleasant situations as well an interactive social survey. In addition to the information technology, methodology and recommendations for urban public open space visitor load assessment is being developed.
Conceptual framework in the area of digital assets: issues and areas of development

Introduction. In recent years, the digital economy has become the main driver of economic growth both at the global and national levels. Within this paradigm, a number of socio-economic challenges are being overcome: creating new jobs, ensuring digital inclusion of people around the world. Problem Statement. The national institutional environment in the field of the digital economy is currently under development, in particular in terms of consistency of conceptual apparatus. The purpose of the article is to analyze the conceptual and categorical apparatus used in the regulatory and legal framework for accounting and taxation of objects arising from the activities of business entities in the digital economy, and to determine the directions of its development. Methods: institutional approach; comparative analysis; synthesis; hierarchical classification method. Results. A bibliographic analysis of scientific and professional publications showed that all categories of “non-fungible token”, “virtual asset”, “cryptoasset” are inherently digital assets, i.e. assets that are stored or transmitted in digital format and have economic or cultural value. The global nature of digital assets has led to the need for international coordination on their legal status and resolution mechanisms. Such digital assets as virtual assets, cryptoassets and their subtypes are regulated at different levels: international (FATF, OECD); regional (EU); national (UK, Ukraine). The presented review of the documents defining the concepts of “digital asset”, “cryptoasset” and “virtual asset” at the international and regional levels, as well as in individual countries, demonstrates that there is currently no generally accepted definition of these terms. Conclusions. The conducted study of the features in forming the conceptual and terminological apparatus of accounting and taxation of objects, the appearance of which is determined by the activities of economic entities under the digital economy, in regulatory documents at various levels showed that when developing institutional support for the regulation of the circulation of digital assets and operations with them in Ukraine the approach used by the FATF was chosen. In terms of its content, the category of “virtual assets” corresponds to the interpretation of the category of “cryptoassets” laid down by European legislation. The author identifies terminological inconsistencies in the Ukrainian legal framework that require further resolution through a cross-analysis of legislation to unify approaches to the interpretation of digital economy objects.
PROSPECTS OF THE MEDVEDKO-AKHMETKINSKY ORE FIELD FOR RARE METAL LITHIUM MINERALIZATION (EAST KAZAKHSTAN)

Lithium is a fast growing sector. It is used in the production of lithium-ion batteries, which are widely used in smartphones, laptops, electric vehicles such as hybrid cars, electric vehicles or e-bikes. The need for this element is dictated by modern demand. The main deposits of lithium, known in Kazakhstan, were explored back in Soviet times, and since then not everyone understood the need for this metal, they were mothballed as a strategic reserve. Such objects include the Medvedka, Akhmetkino deposits, the Shozhe, Lukon, Skalistoye ore occurrence, etc., located within the Medvedko-Akhmetkinsky ore field. The ore field belongs to the central part of the Karagoin-Saryozek pegmatite zone, the Kalba-Narym granitoid belt. Within the field, the main favorable factors for the placement of rare-metal mineralization are considered: geological-structural (tectonic disturbances and ore-bearing environment) and mineralogical-geochemical (indicator minerals of lithium-bearing pegmatites and geochemical elements Li, Rb, Cs, Ta, Nb).
DEVELOPMENT OF INTELLECTUAL ABILITIES OF STUDENTS IN THE PROCESS OF APPLICATION OF GEOINFORMATION TECHNOLOGIES

This article deals with the development of intellectual abilities of students in the process of using geoinformation technology. Digital technologies, in particular geographic information technologies, are increasingly introduced in the educational process of modern schools. They contribute to the learning of mapping skills, the formation of skills to compare, specify, abstract, summarize, establish cause-effect relationships, draw conclusions, develop critical thinking, contributing to the formation of key competencies aimed at self-development and continuing education, the scientific sophistication and geographic culture. The paper reveals such concepts as thinking, intelligence, functions, qualities of intelligence. It covers the psychological and age-specific features of development, the criteria of age periodization, mechanisms of thought, different ways of theoretical description of the children�s development, evaluation of intelligence, types of thinking. Examples of the use of geoinformation technology in geography classes are given.