Reviewerly/exHarmony huggingface dataset:
BASIC TRENDS OF PROFESSIONAL TRAINING OF FUTURE PRIMARY SCHOOL TEACHERS IN THE CONTEXT OF MODERN CHALLENGES

The relevance of the article is determined by the European integration processes that determine the vectors of dynamic development of our society, in particular, higher education and its important component ‒ the system of professional training of future primary school teachers. The article focuses on the trends of professional training of the future teacher in the context of implementing his partnership interaction with subjects of the educational process. The purpose of the article is to identify and analyze the basic trends in the professional training of the future teacher. The article uses such general scientific research methods as: analysis (of psychological and pedagogical literature, information sources for studying educational trends and views of scientists on the problem under investigation); definitional analysis (in order to determine the essence of the researched category); synthesis, comparison, systematization of scientific sources (to identify the state of development of the problem); generalization (to determine the leading category of research). Three levels of basic trends in the professional training of the future primary school teacher for the implementation of partnership interaction of the subjects of the educational process, which are basic in the professional activity of future specialists, are distinguished: social level trends (European integration, humanization and axiologisation of higher education); trends at the scientific and methodological level (polyparadigmatic and interdisciplinary nature of education); practical level trends (technologicalization of higher education). The identified basic trends determine the priority vectors of professional training of primary school teachers in the context of ideas of partnership pedagogy and significantly influence the formation of the professional quality of future primary school teachers necessary for effective partnership interaction between subjects of the educational process.
FORMATION OF INFORMATION AND DIGITAL COMPETENCE OF FUTURE TEACHERS IN THE EDUCATIONAL ENVIRONMENT OF PEDAGOGICAL UNIVERSITIES

The article is devoted to the topical problem of forming the information and digital competence of future teachers in the educational environment of a pedagogical university. The purpose of the article is to justify the need to create a high-quality informational and educational environment in pedagogical universities, which contributes to the formation of informational and digital competence of future teachers. To achieve the set goal, a set of methods was used: analysis and generalization of scientific and pedagogical sources, state documents, results; modeling and synthesis of the obtained data. The article presents the domestic regulatory framework that determines the informatization and digitization of the educational process in institutions of higher education: Recommendations for the implementation of mixed education in institutions of professional pre-higher and higher education (2020); Digital competence framework for citizens of Ukraine (2021); The concept of development of digital competences (2021) and the plan of measures for its implementation was approved; Conceptual and reference Framework of digital competence of pedagogical and research-pedagogical workers (project 2021); A typical program of professional development of pedagogical workers on the development of digital competence (2021); The concept of digital transformation of education and science for the period until 2026 (project 2021). The essence of the concepts «educational environment», «information-educational environment» and the constituent components of the educational environment of a pedagogical university are revealed: information-content, organizational-activity, spatial-subject. The author’s position on the requirements for a high-quality educational environment of a pedagogical university is presented: a high level of professionalism, pedagogical skill, general culture and digital literacy of the scientific and pedagogical staff; availability of a distance education platform in a higher education institution; wide use of modern information technologies; compliance with the rules of etiquette and protection in local and global networks, as information security.
EXHIBITION ACTIVITY IN CONTEXT INFORMATION AND ANALYTICAL SUPPORT OF NATIONAL EDUCATION MODERNIZATION

The article systematizes and highlights the role of exhibition processions in the field of national education. The results of the influence of these measures on the formation of the national identity and self-awareness of educators are revealed. Examples of the main educational exhibitions of Ukraine are given. Exhibition activity with the participation of the NAES of Ukraine as a toolkit of information and analytical support for the modernization of the educational sector is presented. The influential role of exhibitions on the formation of the image of an innovative educator and his development as a personality has been determined. The purpose of the publication is to present the current results of the final stage of planned scientific research on the topic «Bibliographic and analytical support of the activities of the National Academy of Educational Sciences of Ukraine regarding the scientific and methodological support of modernization and reform of education». The authors update the process of studying the problem of educational exhibition activity as one of the effective tools of quality information and analytical support for the modernization and reform of the educational sector of Ukraine. They focus attention on the role of exhibition activity in the formation of the image of both an educator (researcher, innovator, etc.) and directly of an educational institution, a scientific institution. Also, a number of documents of the current legislation of Ukraine regarding the organization of exhibition activities, including in the educational sphere, are given. With the use of content analysis methods of scientific and practical, scientific, informational and reference, etc. sources, as well as event analysis of exhibition activity, systematization and generalization of the obtained research results. Accordingly, the authors present the practical results of analytical investigations regarding the latest publications. Certain aspects from the experience of researchers who focus their own considerations on the study of exhibition processes in their scientific works are also reviewed. In this way, the problem of educational exhibition activity in the context of informational and analytical support and its influence on the formation of the image of both educational institutions and scientific institutions, as well as each researcher, practitioner educator, and everyone who tries to present their own exhibits of practical activity and scientific research in the exhibition environment for positioning the acquired experience and knowledge.
Developing a Secure and Trusted E-Voting System for Libyan Elections

Purpose: The aim of this study is to identify the issues and problems in the current Libyan voting system, and to develop a secure e-voting prototype system associated with the Libyan electoral system and the laws and legislation governing. Theoretical framework: E-voting systems allow greater participation of eligible voters to cast their vote remotely or any location that the voter could be during election. Design/methodology/approach: This study used mixed methodology. A qualitative method via interview would be used to collect qualitative data from citizens and election staff to determine functionalities and features of the proposed system. Using Semi-structured interviews with ten eligible voters, election officials. While a quantitative study conducted to evaluate users and elections of officials to determine how the systems fulfils the requirements and their expectations, to determine if the prototype proposed system offers practical solution to the Libyan voters and if the functionalities of the system fulfil the requirement gathered at the problem definition stage. The second prototype was regarded as meeting the key functional and non-functional requirement and ease of use criteria by users. After fixing the concerns found during the first prototype's evaluation in terms of the requirement criteria. The criterion for ease of use were met. Findings: As a result, 87% of the evaluators indicated that they would adopt the system if it was introduced. Whereas 87% of the evaluators suggested that they would recommend the E-Voting system to their friends, colleagues, and Libyan Election Council. Research, Practical &amp; Social implications: Following the trial of the second prototype, the evaluators proposed that the Libyan E-Voting System (L-EVS) would assist them enable them to exercise their election rights. Originality/value: Concerns and issues from previous political elections have spawned over numerous studies regarding voter confidentiality, voting security, and voting accuracy.
A Tunisian benchmark social media data set for COVID-19 sentiment analysis and sarcasm detection

Abstract The aim of this research is to better understand public perceptions of COVID-19 pandemic patterns and to identify key themes of concern expressed by Tunisian dialect social media users throughout the epidemic. We collected around 23K comments written in Tunisian dialect in both Arabic and Latin letters. These comments were manually annotated by native language experts for sentiment analysis (optimist, pessimist and neutral) and sarcasm detection (sarcastic and non-sarcastic). In addition to health, our data set includes comments relating to additional COVID-19-influenced thematic areas, such as entertainment, social, sports, religion and politics. This paper deals with an extensive analysis of the sentiments and sarcasm expressed in Tunisian social media comments about the novel COVID-19 since its release at the beginning of 2020. On the data set, we also report benchmarking results for sentiment analysis and sarcasm detection using machine learning and deep learning techniques. The best models achieved an accuracy of above 70% on both sentiment analysis and sarcasm detection.
SOLD: Sinhala Offensive Language Dataset

The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially harmful content automatically. These systems require annotated datasets to train the machine learning (ML) models. However, with a few notable exceptions, most datasets on this topic have dealt with English and a few other high-resource languages. As a result, the research in offensive language identification has been limited to these languages. This paper addresses this gap by tackling offensive language identification in Sinhala, a low-resource Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments on this dataset. SOLD is a manually annotated dataset containing 10,000 posts from Twitter annotated as offensive and not offensive at both sentence-level and token-level, improving the explainability of the ML models. SOLD is the first large publicly available offensive language dataset compiled for Sinhala. We also introduce SemiSOLD, a larger dataset containing more than 145,000 Sinhala tweets, annotated following a semi-supervised approach.
Subword-Delimited Downsampling for Better Character-Level Translation

Subword-level models have been the dominant paradigm in NLP. However, character-level models have the benefit of seeing each character individually, providing the model with more detailed information that ultimately could lead to better models. Recent works have shown character-level models to be competitive with subword models, but costly in terms of time and computation. Character-level models with a downsampling component alleviate this, but at the cost of quality, particularly for machine translation. This work analyzes the problems of previous downsampling methods and introduces a novel downsampling method which is informed by subwords. This new downsampling method not only outperforms existing downsampling methods, showing that downsampling characters can be done without sacrificing quality, but also leads to promising performance compared to subword models for translation.
LatentSwap3D: Semantic Edits on 3D Image GANs

3D GANs have the ability to generate latent codes for entire 3D volumes rather than only 2D images. These models offer desirable features like high-quality geometry and multi-view consistency, but, unlike their 2D counterparts, complex semantic image editing tasks for 3D GANs have only been partially explored. To address this problem, we propose LatentSwap3D, a semantic edit approach based on latent space discovery that can be used with any off-the-shelf 3D or 2D GAN model and on any dataset. LatentSwap3D relies on identifying the latent code dimensions corresponding to specific attributes by feature ranking using a random forest classifier. It then performs the edit by swapping the selected dimensions of the image being edited with the ones from an automatically selected reference image. Compared to other latent space control-based edit methods, which were mainly designed for 2D GANs, our method on 3D GANs provides remarkably consistent semantic edits in a disentangled manner and outperforms others both qualitatively and quantitatively. We show results on seven 3D GANs (pi-GAN, GIRAFFE, StyleSDF, MVCGAN, EG3D, StyleNeRF, and VolumeGAN) and on five datasets (FFHQ, AFHQ, Cats, MetFaces, and CompCars).
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency

We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the $\bf{Jacobian}$ of this composition from the identity matrix. This regularizer -- $\texttt{GradICON}$ -- results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol.
Tren publikasi collaborative governance sebuah analisis bibliometrik

Introduction: This study aims to analyze publication trends related to collaborative governance indexed in Scopus database from first year of publication until June 2022.&#x0D; Data Collection Methods: Collaboratove Governance field journal articles published between 1977-2022 were searched from the scopus database using "Collaborative Governance” keyword.&#x0D; Data Analysis: This study uses an analysis of publication trends in collaborative governance, the most productive countries and the most productive organizations, Co-Authorship, Co- and Citation.&#x0D; Results and Discussion: Research related to collaborative governance widely are conducted globally. Multistakeholders need to collaborate to solve the challenges. The year 2021 becomes the most productive year with 700 publications. United States and United Kingdom are the two most productive countries. The co-authorship network shows Torfing while Ansell was the most cited author. The Co-Occurrence Network, based on keywords, shows governance, collaboration and collaborative governance. &#x0D; Conclusion: Bibliometric methods can be used to analyze publication trends related to "Collaborative Governance". However, it needs the support of datasets and a complete computing system. The results of the analysis of articles related to collaborative governance there is an increase in publication trends related to collaborative governance.
A new adaptive causal consistency approach in Edge Computing Environment

Edge computing is a new computing paradigm that has emerged to offload computation and storage to edge devices in order to get a shorter response time and more efficient processing.Nevertheless, adopting a consistency scheme that can conserve multiple replicas while guaranteeing a good level of consistency is an open issue.Moreover, a data store with only one consistency level is not suitable for applications that have different consistency requirements.In this paper, we propose MinidoteACE, a new adaptive consistency system which is an amelioration version of Minidote a causally consistent system for edge applications.Unlike Minidote which supports only causal consistency, our model allows applications to run also queries with stronger consistency guarantees.Experimental evaluations show that throughput decreases only by 3.5% to 10% when replacing causal operation with a strong operation.However, update latency increase significantly for strong operations up to three times for 25% update workload.
An Image Enhancement Method Using Nonlinear Function

Image enhancement plays an important role in image processing. This paper proposed an image enhancement method using a nonlinear(exponential) function. Firstly, we use the exponential function to construct the gray transformation equation. Then, depending on the position of each pixel, the gray scale obtained by gray transformation is further adjusted based on the compression factor. In section 3, the comparative experiment shows that the proposed method could achieve good performance.
Rhyme Frequency in Nineteenth-Century English Poetry

To account for the effects of rhyme, analysis of word frequencies in poetry should distinguish line-end words from words found elsewhere in the text.This research explores three methods of analyzing rhyme word frequencies that can contribute to the understanding of the conventions that shaped nineteenth-century English verse.Rhyme frequency ranking, effect size metrics, and the rhyme frequency ratio offer distinct views of a large poetry corpus and glimpses into how historical readers might have experienced rhyme's structuring force within poetic discourse.
MILITARY APPLICATIONS OF COGNITIVE SCIENCES: COGNITIVE WARFARE, A MATTER OF PERCEPTION AND MISPERCEPTION

he concept of cognitive warfare is steadily gaining more and more attention in security and defense discourse. By employing a cognitive psychology perspective, the article is examining how perception and/or misperception factor into cognitive mapping of strategic and operational concepts. The central aim of the paper is to outline that the approach to the human domain is deeply rooted in nationally conditioned perception. The article is looking to achieve this academic goal by comparing the NATO conceptualization of cognitive warfare to the Russian Federations'. The structure is as follows: first there is a brief review of the cognitive psychology paradigm, especially key concepts as they relate to the scope of the paper. The subsequent sections compare the NATO cognitive warfare approach to the opposing view, namely the one of the Russian security and defense apparatus. The "special military operation" in Ukraine initiated by the Kremlin in 2022 confirmed that in the event of a territorial aggression against a NATO member state, the alliance as a whole remains the basis of European security and defense posture. It is for this very reason that the alliance ought to objectively analyze how Russian decision makers perceive Western perspective in the cognitive realm.
Kajian Perbandingan Penggunaan Aplikasi Lumion dan V- Ray for Sketchup Bagi Penghasilan Visual Persembahan Akhir Reka Bentuk Seni Bina

In this fast-developing era of digital and visual technology, architectural graphics software has become increasingly sophisticated and is widely used in industry as well as in higher education; not only to facilitate the user but to get better, consistent and fast graphic results. Students majoring in architecture at the tertiary level are also no exception in ensuring that they are not left behind from following the latest technological developments, especially current technologies related to their learning process. The graphics softwares available in the market which have become more complex and specialized over the years have also proven to help architecture students in ensuring the delivery process becomes easier and better if the right software is selected and used by students. It is undeniable that if less suitable software is used by students, it will affect the end result of the desired design presentation. This study aims to make a comparison between the usage of two graphics software that are widely used by architecture students at present, namely Lumion 8.0 and V-Ray 3.4 for Sketchup, and identify the advantages and disadvantages of its rendering process. To achieve the purpose of the study, a recording of the rendering processes of both Lumion 8.0 and V-Ray for 3.4 SketchUp was made on a specific building design for the final project of an undergraduate architectural design studio course. In general terms, the results found that the advantage of Lumion 8.0 lies in the faster rendering time, while the advantage of V-Ray 3.4 for SketchUp lies in the quality of the rendering details produced.
Compound Tokens: Channel Fusion for Vision-Language Representation Learning

We present an effective method for fusing visual-and-language representations for several question answering tasks including visual question answering and visual entailment. In contrast to prior works that concatenate unimodal representations or use only cross-attention, we compose multimodal representations via channel fusion. By fusing on the channels, the model is able to more effectively align the tokens compared to standard methods. These multimodal representations, which we call compound tokens are generated with cross-attention transformer layers. First, vision tokens are used as queries to retrieve compatible text tokens through cross-attention. We then chain the vision tokens and the queried text tokens along the channel dimension. We call the resulting representations compound tokens. A second group of compound tokens are generated using an analogous process where the text tokens serve as queries to the cross-attention layer. We concatenate all the compound tokens for further processing with multimodal encoder. We demonstrate the effectiveness of compound tokens using an encoder-decoder vision-language model trained end-to-end in the open-vocabulary setting. Compound Tokens achieve highly competitive performance across a range of question answering tasks including GQA, VQA2.0, and SNLI-VE.