Reviewerly/exHarmony huggingface dataset:
The role of virtual platforms in modern astronomy education: analysis of innovative approaches

The purpose of the article was to study the role of virtual platforms in modern astronomy teaching by identifying and using innovative approaches as well as conducting an empirical study on this topic. To achieve this goal, the scientific methods of descriptive statistics and comparative analysis were used. Subsequently, qualitative interviews were transcribed, and the thematic analysis was used in order to identify recurring themes and patterns in the data. At the same time, a content analysis of the professional literature (41 items) on the integration of virtual platforms into the educational process (including the use of its achievements for teaching astronomy) was carried out. The results showed that the use of virtual platforms in the process of teaching astronomy is one of the promising innovative technological trends that requires further study. The study found that almost all the surveyed specialists used digital tools in their teaching and learning process at least once a week, which is in line with international standards. The interest in the latest technologies, such as artificial intelligence, indicates the relevance of global trends in education in Ukraine, especially in the context of astronomy. In this regard, it is important considering the issues of corporate ethics, the introduction of new approaches to the organisation of the educational process, structural and managerial changes, and pedagogical strategies aimed at adapting to new realities. The scientific novelty of the paper is the use of empirical data to determine the role of virtual platforms in teaching astronomy in Ukraine. The conclusions indicated that these innovative approaches contribute to the improvement of the level of astronomy understanding and to the increase of students' interest in learning about the universe. They allow creating more dynamic and interactive lessons for the learning process.
Ml Based Hand Gesture Recognition

The projects focus on Gesture recognition using the media pipe hands Lite framework where the custom data set of 10 gesture Images is trained with an in built media pipe hands model which contains 2CNN models-A palm detection model running on single Shot detection architecture and a hand landmark generator running on regression model architecture. The data set is successfully trained and tested with the proposed method and an accuracy of 98 percent is obtained. Keywords: Gesture Recognition, Media pipe, Hands De-tection, CNN, Regression Model, Hand Landmark Detection, Machine Learning, Image Processing.
Geochemical features of sulfides of the Aktash magnetite-polymetallic deposit, Western Karamazar, Tajikistan (LA-ICP-MS data)

The distribution of trace elements in sulfdes from various ores of the Aktash magnetite-polymetallic deposit (Tajikistan) is studied using LA-ICP-MS method. Sulfdes partly replace banded magnetite ores. Each sulfde type exhibits specifc geochemical features. Galena from sulfde-magnetite ores contains the higher amount of elements of «high-temperature» association (Bi, Ag, Cu) than galena from pyroxene-sphalerite ores, which, in addition to Ag and Bi, also contains higher amount of elements of «medium-temperature» association (Se, Te, and Sb). Sphalerite from sulfde-magnetite and chlorite-pyrrhotite ores contains higher Fe, Cd and Mn amount than sphalerite from pyroxene-sphalerite ores, which is characterized by elevated Co and As contents. Pyrite is depleted in trace elements except for As, the content of which is maximum compared to other sulfdes. The Ag, Se, Bi, Zn, Cd and Ni contents of chalcopyrite from chlorite-pyrrhotite ores are higher than in chalcopyrite from sulfde-magnetite and pyroxene-sphalerite ores. Chalcopyrite from pyroxene-sphalerite ores is characterized by elevated Pb, As, Ge, Te and Sb contents. Relatively high Co, Ni and Se contents are determined in pyrrhotite. In addition to Fe, Cu, Zn, Pb and Au, which form minerals in all types of ores, we suggest by-product extraction of following elements from complex sulfde-magnetite, chlorite-pyrrhotite and pyroxene-sphalerite ores (the Bi, Ag, Se and Te contents of galena and Cd content of sphalerite are shown in brackets): Bi (19520–24650 ppm), Ag (7907–9650 ppm), Se (397–606 ppm) and Te (276– 436 ppm) from galena concentrate and Cd (8525–27670 ppm) from sphalerite concentrate.
Handwritten Digit Recognition Using CNN

This project explores the application of Convolutional Neural Networks in Handwritten digit recognition. Exploiting the widely used MNIST dataset, we proposed a deep learning model that encompasses the Ensemble model that is capable of predicting and recognizing the handwritten digits and achieves remarkable precision. The dataset contains 70000 images in total, where 55000 images are for training, 5000 images for validating, and 10000 images for testing. By using CNN, we surpassed the traditional way of training the algorithm with machine learning. By incorporating robust pre-processing techniques and innovative training ideas and strategies, our model showcases resilience to real-world problems and challenges which includes reading the postal codes in the written mail, reading the digits in the handwritten checks, digits drawn on the mobile touch panel, etc. To better extract the features of the complex handwritten digits, bagging is used from the Ensemble techniques. The objective of this project is to find a better optimizer to work with ensemble models, which encompasses the merging of optimizers with the deep learning models. Key Words: Handwritten digits, CNN, Ensemble Model, Optimizers, MNIST.
Detection and Classification of Leukemia using Machine Learning

Abstract—Leukemia is a haematological malignancy characterized by the abnormal proliferation of cancerous cells inside the bone marrow, resulting in the disruption of normal blood cell production. It induces a rise in the number of leukocytes. Leukemia is characterized by an aberrant proliferation of white blood cells. Leukemia is a heterogeneous disease characterized by the presence of several subtypes. Certain forms of leukemia have been observed to impact the paediatric population. Adults can be affected by several forms of leukemia. Leukemia is categorized according to the progressive elevation of white blood cell counts. There are two main classifications of leukemia, namely Acute leukemia and Chronic leukemia. When the rate of progression is gradual, individuals are diagnosed with chronic leukemia. Acute leukemia is characterized by a rapid proliferation of white blood cells. These two categories are further subdivided into two distinct subtypes for each form of leukemia. Our models were constructed using a customized version of the VGG13 architecture. The VGG13 architecture is a widely used Convolutional Neural Network (CNN) model that consists of many layers. The object recognition model is considered to be innovative. The models are utilized to categorize the microscopic blood smear images provided as input into three distinct categories: Acute Lymphocytic Leukemia or normal cells. The model achieves 92% accuracy in classifying ALL cells assisting automated identification of leukemic cells. Keywords— leukemia detection, leukemia classification, VGG, CNN, Acute leukemia
Artificial Intelligence Powered Writing Tools as Adaptable Aids for Academic Writing: Insight from EFL College Learners in Writing Final Project

This research investigates the viewpoint of English as a Foreign Language (EFL) students about using Artificial Intelligence (AI) as writing aids for their culminating project. This study utilized a convenience sampling method to recruit 50 8th-semester students from public and private colleges in North Sumatra, Indonesia, who were completing their final assignments. Data was collected using open-ended questionnaires and interview approaches, while data analysis was performed using SPSS version 29.0. The results indicate that students favor the utilization of Artificial Intelligence in the composition of their culminating assignment. Despite the scarcity of Artificial Intelligence, it does not diminish their favorable perspective on the necessity of employing Artificial Intelligence as a writing aid. Students' positive perception of utilizing Artificial Intelligence in composing their final assignments influences the efficacy and caliber of their written work. Hence, the favorable viewpoint expressed by students regarding the utilization of Artificial Intelligence underscores the numerous benefits these writing tools offer in the completion of their final projects.
Diacritic Restoration for Yoruba Text with under dot and Diacritic Mark Based on LSTM

Abstract&#x0D; Yoruba is a tonal language spoken primarily in Nigeria, some West African countries, and other parts of the world by over 40 million people. Many Yoruba texts written online lack tone marks, which can be confusing, ambiguous, and difficult for Natural Language Processing. This paper presents a method, which combines syllable-based approach and long short-term memory (LSTM) for diacritics restoration of standard Yoruba text.By enhancing the built-in varnishing gradient of RNN, the aim is intended to recover lost diacritics in Yoruba text for both characters that carry diacritic signs and underdot and return it with the proper diacritics. Data were acquired from Yoglobavoice, BBC Yoruba new and Yoruba words collected from literate indigenous writers. 27050 Yoglobalvoice datasets, 2000 Yoruba words extracted from BBC Yoruba news, and 1470 Yoruba words collected from a Yoruba language teacher.In addition, syllabic module was developed to group the tokenized word into different syllables. The output of the syllabication algorithm was fed into the Long Short-Term Memory (LSTM) module for training, the LSTM model was trained using 70% of the dataset and validated using 30% of the dataset. The result obtained showed 96% accuracy. From the result, it was observed that the use of LSTM for restoring diacritic gave an improved restoration of both character with under dot and character that contains tone-marks.
On the eternal non-Markovianity of non-unital quantum channels

The eternally non-Markovian (ENM) Pauli channel is an example of a unital channel characterized by a negative decay rate for all time [Formula: see text]. Here, we consider the problem of constructing an analogous non-unital channel, and show in particular that a [Formula: see text]-dimensional generalized amplitude damping (GAD) channel cannot be ENM when the non-Markovianity originates solely from the non-unital part of the channel. We study specific ramifications of this result for qubit GAD. Specifically, we construct a quasi-ENM qubit GAD channel, characterized by a time [Formula: see text], such that the channel is non-Markovian (NM) only and for all time [Formula: see text]. We further point out that our negative result for the qudit GAD channel, namely, the impossibility of the eternal NM property, does not hold for a general qubit or higher-dimensional non-unital channel.
A class of t-weight codes and its applications

In this paper, we constructed a class of [Formula: see text]-weight linear codes over [Formula: see text] under the homogeneous weight metric by their generator matrices, where [Formula: see text] and [Formula: see text] The Gray images of some class of these codes over [Formula: see text] are [Formula: see text]-ary nonlinear codes, which have the same weight distributions as that of the two-weight [Formula: see text]-ary linear codes of type SU1 in the sense of [R. Calderbank and W. M. Kantor, The geometry of two-weight codes, Bull. London Math. Soc. 18(2) (1986) 97–122]. Also, we obtained the minimum distance of the dual codes of the constructed codes. Further, we discussed some optimal linear codes over [Formula: see text] with respect to Plotkin-type bound from the constructed codes when [Formula: see text] Furthermore, we investigated the applications in strongly regular graphs and secret sharing schemes.
Komitmen Membangun Jaringan Kerjasama Perpustakaan Perguruan Tinggi

Universities must have libraries to encourage student literacy, the library is a location for a collection of information provided to add insight that is not known, in this study the researchers looked at how library collaboration was built for the sake of fulfilling information in the library, collaboration is one of the supports for libraries to meet information needs which exists. Along with the times, Indonesia is also developing, one of which is technology, so libraries use technology to facilitate collaboration, through library collaboration networks it is very easy to do, even at a long distance, libraries are very easy to access by other people, one example is the establishment of organizations such as FPPTI and FKP2TN. This research shows how universities carry out collaboration through networks such as digital and others.&#x0D; Keywords: Libraries, Network cooperation, Universities
ՀԵՆԱՆԻՇԱՎՈՐՄԱՆ (ԲԵՆՉՄԱՐՔԻՆԳԻ) ԳՈՐԾԻՔԻ ՕԳՏԱԳՈՐԾՄԱՆ ՀՆԱՐԱՎՈՐՈՒԹՅՈՒՆՆԵՐԸ ՄԱՍՆԱԳԻՏՈՒԹՅԱՆ ԿՐԹԱԿԱՆ ԾՐԱԳՐԵՐԻ ԲԱՐԵԼԱՎՄԱՆ ԱՇԽԱՏԱՆՔՆԵՐՈՒՄ / POSSIBILITY OF USING BENCHMARING FOR THE IMPROVEMENT OF THE PROFESSIONAL EDUCATION PROGRAMS

Benchmarking fosters an "outside" view of the internal issues of the higher education institution. It is a continuous improvement process, so both the method and the toolkit chosen are important. Currently, various approaches and types of benchmarking are used, each of which has its own theoretical foundations and tools, including digital ones. An important factor in the process of higher education reforms is the modernization of the tools used for the development, implementation and comparison of competitive professional education programs with the best ones of the same content. Therefore, benchmarking of the the professional education programs is important as a tool for identifying the need for changes. The purpose of the research is to carry out benchmarking of the two professional education programs chosen in the framework of the research by studying the theoretical bases of benchmarking, types and features of its implementation and using a digital tool. The recommendations made on the basis of results of the research will foster the alignment of other professional education programs of Brusov State University (BSU) with the demands of the learners and the labor market, which will increase the competitiveness of the university and engagement of students.
Real-Time Map Compression Method Based on Boolean Operation and Moore-Neighborhood Search

Presently, the topics of sensor network information sharing and multi-robot mapping in robotics have gained considerable research interest. This paper introduces a real-time map compression methodology developed to address the time-intensive problem associated with large-scale map cross-system transmission. This method employs neighborhood filtering, a modified Moore-Neighborhood tracking algorithm, and polygonal Boolean operation technology to extract the inner and outer boundaries of a 2D raster map, thereby enabling map reconstruction. The proposed methodology specifically focuses on the removal of burrs from the outer boundary and reoptimization of the inner boundary search process. This is done to enhance the efficacy of the map reconstruction. Additionally, the extracted inner and outer boundaries are utilized for the reconstruction of the map. The performance of the proposed algorithm was evaluated using several types of maps. The results indicate a relatively low execution time of 300 ms for a map comprising 2 million pixels, which essentially fulfills the real-time requirements for large-scale map transmission and reconstruction. Furthermore, the quality of the map reconstruction was assessed using the Universal Image Quality Index (UIQI), Multi-Scale Structural Similarity (MS-SSIM), and Peak signal-to-noise ratio (PSNR) image evaluation metrics. The reconstruction results reveal that the map effectively satisfies the requirements with a UIQI exceeding 0.9984, an MS-SSIM surpassing 0.9282, and a PSNR above 27.7089. Simultaneously, the map’s compression rate exceeded 99.0%, thereby demonstrating that the algorithm can accomplish a significantly high compression rate. This evidence suggests the algorithm’s potential for effective deployment in real-world applications requiring large-scale map transmission and reconstruction.
Application of virtual reality and multimedia integration in piano teaching of sound education major in colleges and universities

The research has organically integrated VR and multimedia technology, built a corresponding piano teaching platform, and verified its effectiveness through experiments. The experimental results show that the classification effect, classification accuracy and recognition rate have been significantly enhanced after the internal selection algorithm of VR-MM is improved. Among them, the VLRAMM-BPNN algorithm, combining the two improved algorithms with BPNN, has the best performance, the highest coincidence between actual and predicted classification results, the highest classification accuracy of 95.93%, and the recognition rate is 21.6% higher than that of BPNN. According to the overall performance test of VR-MM, both the response speed and various stress tests have achieved the desired results. When the number of people is set at 10,000, the pressure has also reached the bottleneck, fully meeting the actual needs of piano teaching. To sum up, the performance of VR-MM has achieved the expected effect.
Uncovering the Effects of the Russia-Ukraine Conflict on Cryptocurrencies: A Data-Driven Analysis with Clustering and Biplot Techniques

This paper examines the impact of the Russia-Ukraine armed conflict on cryptocurrencies, focusing on the period between December 2021 and April 2022. Utilizing data from platforms monitoring the cryptocurrency market, the study evaluated fluctuations in major cryptocurrencies in relation to various variables, aiming to understand how the conflict has influenced them. Clustering and HJ-Biplot techniques were employed in the analysis. The findings revealed a decrease in valuation across all cryptocurrencies in 2022, with a particularly sharp drop in February, the month when the conflict started. Although there was an increase in valuations from March onwards, the analysis indicates that cryptocurrencies have not recovered to their value at the end of 2021, and they continue to be valued less overall.
Unraveling the Power of 4D Residual Blocks and Transfer Learning in Violence Detection

In recent years, action recognition has seen significant advancements in using Convolutions Neural Networks (CNNs) models for video analysis. One of the essential fields in this area is violence detection, which determines whether or not violent scenes use videos from surveillance cameras. One popular approach to handle this is the Flow Gated Network, two separate networks that extract the features from the frames and the optical flow of the videos using convolutions. However, it cannot capture the spatio-temporal characteristics of the video, which are crucial for accurate action recognition. To address this limitation, researchers have proposed using 4D convolutions at the video level (V4D) and stream buffer in the case of MoViNets. These networks are designed to preserve the 3D spatio-temporal representation of the video while also incorporating residual connections, which allow for better feature propagation and improved performance. In this work, we propose using 4D residual blocks and MoViNets for violence detection on the data set RWF-2000 to achieve state-of-the-art results in action recognition. Furthermore, this approach compares the strengths of MoViNet and V4D, resulting in more robust and used models for violence detection getting 0.885 for 4D residual block and 0.895 for MoViNet A1 in terms of accuracy, beating the benchmark architecture.
"The role of continuous education system in the society in the improvement of personal ecological culture"

Mazkur maqolada inson va tabiat munosabatlari tizimida ekologik taʼlimni rivojlantirishning ijtimoiy ahamiyati tadqiq etilgan. Shuningdek, ekologik taʼlimni yuksaltirish asosida ekologik ongli faoliyatni shakllantirishning ustuvor vazifalari tahlil qilingan. Ijtimoiy-ekologik muammolar yechimida zamonaviy innovatsion yondashuvlar bilan bir qatorda mahalliy aholining tabiatdan foydalanish anʼanalariga asoslangan ekologik yondashuvning ahamiyati ilmiy asoslangan. Ekologik madaniyatga doir taʼlim-tarbiya va targ‘ibot-tashviqot ishlari faoliyatini takomillashtirish bilan bir qatorda o‘zbek xalqi ekologik qadriyatlarini tiklash va rivojlantirish tendensiyalarining ilmiy-nazariy asoslarini ishlab chiqish va rivojlantirish imkoniyatlarining nazariy metodologik va amaliy jihatlarini chuqurlashtishga oid ilmiy xulosalar berilgan.
High-capacity reversible data hiding by recursive look-ahead in encrypted images adaptive MSB prediction for the cloud computing

&lt;p&gt;The present research proposes a high-capacity reversible data embedding technique (RDH-EI) for applications utilizing cloud storage that use a recursive look ahead adaptive MSB prediction approach. The paper also proposes a three-level image encryption approach where on the first level, the image is subdivided into 4 × 4 blocks and the blocks are permuted. In the second level, two different neighbourhoods namely the middle neighbourhood and peripheral neighbourhood are estimated, from which the elements of the middle neighbourhood are permuted within the respective neighbourhood. In the third level, the elements of the peripheral neighbourhood without including the elements of the middle neighbourhood are permuted within the respective neighbourhood. The image that was encrypted by the image owner was uploaded to the cloud. Out of five different neighbourhoods estimated on the 4 × 4 blocks, the best neighbourhood is then estimated from the encrypted image which provides a maximum embedding capacity. The data is then embedded in the best neighbourhood using the adaptive MSB prediction algorithm. Once the embedding iteration is completed, the marked encrypted image obtained on the previous iteration is again evaluated for the best neighbourhood. This recursive process is repeated till the embedding capacity of the best neighbourhood is less than the threshold. The standard test images from the datasets BOSS base, UCID and BOWS-2 were used to evaluate the proposed approach using measures like embedding rate, SSIM and PSNR. The suggested method offers an average embedding rate that is greater than previous recent RDH-EI approaches, at 3.714 bpp, 3.4826 bpp and 2.9412 bpp for the datasets BOSS base, BOWS-2 and UCID, respectively.&lt;/p&gt;
Hierarchical WaveFunction Collapse

Video game developers are increasingly utilising procedural content generation (PCG) techniques in order to generate more content far quicker than if it were designed. Although promising, much of the successful work to date has been achieved in simple 2D environments or has required significant hand-designed effort. This is due to the difficult nature of defining plausible metrics, fitness functions or reward functions which can quantify the quality of generated levels. Our work aims to avoid this difficulty by utilising minimal human design to build up constraints, and generating diverse levels that maintain these constraints. We achieve this by hierarchically applying the recent WaveFunction collapse (WFC) algorithm. Our approach allows designers to specify larger-scale components, and additional constraints that are difficult to enforce using standard WFC. We empirically demonstrate that our approach does indeed incorporate these higher-level structures, and is more controllable than our baselines. Despite these benefits, our levels do not suffer from a lack of diversity. Finally, we illustrate the scalability and flexibility of our approach by applying it to both 2D and 3D domains.