Reviewerly/exHarmony huggingface dataset:
Multicenter automatic detection of invasive carcinoma on breast whole slide images

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice.
Roundtable Technique and its Correlation with EFL Preparatory School Students' Achievement in Writing Hortatory Exposition Text

Foreign language instruction methodology has been varying over the years; however, there is no one single best technique to use in teaching English as a foreign language.The best language teaching method depends on several issues such as: learners' levels, learning processes, teacher competencies, and the aims of learning.The present study aims at investigating the effectiveness of using Roundtable Technique for teaching Hortatory Exposition Text (HET) to preparatory school students through:1.Finding the average level of the fifth year-scientific preparatory school students' achievement in writing HET.2.Finding whether there is any significant difference between the achievement of the experimental group and that of the control group, in writing HET.3.Finding whether there is any significant difference between the students' achievement in writing HET at the recognition level and that at the production level, of the posttest.
A Computer-Based Method to Improve the Spelling of a Learner with Dyslexia

For students, academic writing is a must skill to be honed to achieve good grades from teachers and majority novice learners commit errors in spellings. Such students with spelling errors lag behind other peers. In this paper we present a method which aims to improve the spelling of children with dyslexia through playful and targeted exercises. Manual correction of spelling errors involves a person manually checking and correcting misspelled words. Auto correction is faster and more efficient than manual correction, but it may also introduce new errors and may not always be accurate. This case study aimed to investigate the influence of word-office as a tool on spelling improvement and accuracy as while writing in word-office misspelled word is highlighted and student can correct spelling. For this purpose, we conducted research on one student Grade 8 with spelling difficulties in a special school for special needs to participate in writing essays. This research took the form of writing manually on a paper and typing another essay with the use of word-office software. The study concludes that there is reasonable amelioration in student’s achievement in correction of spellings with the use of typing spelling. Furthermore, this research has implications for the development of new technology and software designed to support learners with dyslexia.
Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

At the era of digital transformation, fraud has dramatically increased, notably in the banking industry. Annually, it now costs the world's economies billions of dollars. Daily, news of financial fraud has a negative influence on the world economy. According to the harsh loss caused by fraud, effective strategies and methods for avoiding income statement fraud have to be implemented. Also, the procedure of identification should be applied. This is regarded as a result of the development of modern technology, modern invention, and the rapidity of global communications. Actually, deterrent technologies are most effective to reduce fraud and overcome cons. So, it is necessary to find ways to overcome such deterrence by depending on developed methods to identify fraud. Data mining techniques are currently the most widely used methods for the prevention and detection of financial fraud. The use of datasets for fraud detection complies with the norms of data mining, which include feature selection, representation, data gathering and management, pre-processing, comment, and summative evaluation. Methodologies for identifying fraud are essential if we want to catch criminals after fraud prevention has failed. The greatest fraud detection strategies for locating loan banking and financial fraud are compared in this article.
Determining conditions for improving the quality of education in rural schools with low educational outcomes

Introduction. The authors investigate the problem of improving the quality of teaching and learning in comprehensive schools. The purpose of the study is to determine the conditions for improving the quality of education in rural schools with low educational outcomes. Materials and Methods. The research investigation follows systemic, student-centered, activity-oriented and environmental approaches, which are adopted in accordance with the context of the study in the concepts of personalized learning, the development of information and communication culture, open education, interaction and cooperation of a regional pedagogical university with rural and small schools. The research methods used include scientific literature analysis, comparison, concretization, synthesis of conclusions and generalization, empirical research methods, methods of quantitative and qualitative analysis of the results obtained. The developed model is used by the authors as a universal technology for forecasting, planning and solving the problem of cognitive uncertainty. It fulfills a methodological function and is considered as a universal variant of specially organized activities to minimize risks when achieving the desired high educational results in rural educational institutions. Results. The authors summarize the trends and priorities of improving the quality of education in educational institutions based on the analysis of Russian and foreign studies. The authors summarize the ideas about the modeling of teaching and learning processes in the education system. In the course of the study, the authors identify the conditions for a universal variant of the model aimed at improving the quality of education for rural schools with low educational outcomes as a regional management tool. Based on the developed and tested model for improving the quality of education and the results of preliminary comprehensive diagnostics for each educational organization of a single region, the authors determine the most characteristic regional risks using the example of the Republic of Bashkortostan. It is noted that one of the effective mechanisms for improving the quality of education in general education institutions include activities that take into account the specifics and risk of a particular region. Conclusions. The study concludes that the developed model is a conceptual support for building the process of obtaining high educational results in rural schools. The results obtained can be used by local educational authorities of the subjects of the Russian Federation, federal districts of the Russian Federation, and educational institutions to improve the quality of education.
Attention-Based Bi-Prediction Network for Versatile Video Coding (VVC) over 5G Network

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively.
Lorentz quantum computer

A theoretical model of computation is proposed based on Lorentz quantum mechanics. Besides the standard qubits, this model has an additional bit, which we call hyperbolic bit (or hybit in short). A set of basic logical gates are constructed and their universality is proved. As an application, a search algorithm is designed for this computer model and is found to be exponentially faster than Grover’s search algorithm.
Coalitional Federated Learning: Improving Communication and Training on Non-IID Data With Selfish Clients

In this article, we propose a new paradigm of Federated Learning (FL) for Internet of Things (IoT) devices called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Coalitional Federated Learning</i> . The proposed paradigm aims to address the challenges of (1) non-independent and identically distributed (non-IID) data across clients; (2) communication overhead due to the large number of messages exchanged between the server and clients; and (3) selfish clients that seek to obtain the latest global models without efficiently contributing to the training of the FL model. Our novel paradigm consists of three main components, i.e., (1) client-to-client trust establishment mechanism that relies on subjective and objective sources to enable clients to establish credible trust relationships toward one another; (2) trust-enabled coalitional game to enable clients to autonomously form harmonious coalitions of FL trainers; and (3) coalitional federated learning in which multiple local aggregations take place at the level of each coalition to mitigate the problems of non-IID data and communication bottleneck. Extensive experiments suggest that our solution outperforms both the standard vanilla FL approach and one state-of-the-art trust-based FL approach in terms of increasing the accuracy of the global FL model and decreasing the presence of selfish devices participating in the training.
Online compiler «Replit» usage during the study of the programming discipline

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 2. 388 pages.
Unsupervised anomaly detection for posteroanterior chest X-rays using multiresolution patch-based self-supervised learning

Abstract The demand for anomaly detection, which involves the identification of abnormal samples, has continued to increase in various domains. In particular, with increases in the data volume of medical imaging, the demand for automated screening systems has also risen. Consequently, in actual clinical practice, radiologists can focus only on diagnosing patients with abnormal findings. In this study, we propose an unsupervised anomaly detection method for posteroanterior chest X-rays (CXR) using multiresolution patch-based self-supervised learning. The core aspect of our approach is to leverage patch images of different sizes for training and testing to recognize diverse anomalies characterized by unknown shapes and scales. In addition, self-supervised contrastive learning is applied to learn the generalized and robust features of the patches. The performance of the proposed method is evaluated using posteroanterior CXR images from a public dataset for training and testing. The results show that the proposed method is superior to state-of-the-art anomaly detection methods. In addition, unlike single-resolution patch-based methods, the proposed method consistently exhibits a good overall performance regardless of the evaluation criteria used for comparison, thus demonstrating the effectiveness of using multiresolution patch-based features. Overall, the results of this study validate the effectiveness of multiresolution patch-based self-supervised learning for detecting anomalies in CXR images.
The Improvement in Poetry Writing Skills by Using Prezi in the Primary School

Writing is one of the subjects taught at school. Through poetry writing, students’ imaginative writing skills can be improved. The purpose of this research is to describe the improvement in poetry writing skills and students’ activity using Prezi. This research employed a classroom action research method conducted in three cycles. Each cycle consisted of four stages, namely: planning, implementation, observation, and reflection. The subjects of this research were fifth-grade students of a primary school in Inderalaya consisting of 13 male students and 16 female students. The instrument of the data collection was a poetry writing test. The results of the three cycles in this research were 44.44%, 78.57%, and 89.28% respectively. It showed an increase in poetry writing skills of fifth-grade primary school students after the use of Prezi media in learning. Hence, it can be concluded that Prezi was an appropriate media to teach poetry writing. It is recommended that Prezi could be used as an alternative media to teach writing in the primary school.
A Lexicon-based Feature for Twitter Sentiment Analysis

Twitter Sentiment Analysis shows several challenges due to the platform’s features (e.g., short messages, colloquial style, etc.). People want to express their ideas related to personality, events, or breaking news. Social media is one of the fastest ways to express opinions, and research directions are developed to analyze the polarity of written messages. Very important for this domain is how you process the data and define features that can mine valuable information from textual inputs. Exploring various features and combining them can increase the quality of the entire methodology. Hence, a new system is designed to build a lexicon-based feature for detecting the polarity of a tweet. Therefore, messages posted by a user on Twitter are enhanced with a sentiment indicator provided by a lexicon. Then, the new model will be used by a classification algorithm. The numerical experiments are developed on several different datasets in terms of size and topics. The results highlight that the defined feature outperforms other lexicon-based features from literature. Moreover, the experiments based on the sentiment indicator produce better performance values than the traditional approaches that use the original tweet without additional features.
Credit Card Fraud Detection Using Machine Learning, Deep Learning, and Ensemble of the both

It is the responsibility of the credit card companies to ensure that their customers are given the best security measures when it comes to fraudulent credit card transactions. The main objective of this work is to explore whether a credit card transaction (before being processed) is fraudulent or not. And if it is found out to be fraudulent, then the credit card owner must be notified of the same. The approach used is to make use of highly imbalanced and skewed transactional data and train various models available in ML as well as a DL model for the detection of frauds and finally compare their accuracies and test results and choose the most suitable model. The system currently used to detect fraud is plagued by misclassifications and highly false positives. So, our motive is to not miss any fraud cases (high recall) as well as not predict too many non-fraud cases as fraud cases (high precision) as if this happens, it will lead to poor reviews for the concerned credit card company. We demonstrate various methods to deal with the imbalance of the data such as choosing appropriate metrics for evaluation of models and resampling of data.
A hybrid deep learning approach for phenotype prediction from clinical notes

Identifying patient cohorts from clinical notes in secondary use of electronic health records is a fundamental task in clinical information management. The patient cohort identification process requires identifying the patient phenotypes. However, the growing number of clinical notes makes it difficult to analyze the data manually. Therefore, automatically extracting clinical concepts is crucial to identify patient phenotypes correctly. This paper’s proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patients’ phenotypes in clinical notes. Furthermore, an extra CNN layer is run parallel to the hybrid proposed model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding’s performance in identifying patient phenotypes. We also measured the effect of adding additional data cleaning steps on discharge reports to identify patient phenotypes using deep learning models. Results demonstrated the proposed hybrid model extracts more features than existing methods of patient phenotype extraction and provides a better F1-score. We show that complementing the proposed hybrid model with an extra CNN in identifying different phenotypes improves the F1 scores. In addition, eliminating punctuation, numbers, and stop words in discharge reports before training hybrid models increased model performance.
Personalized and Precision Medicine 2022

This Special Issue, “Personalized and Precision Medicine 2022” (https://www [...]
Comparison of the National Curriculum from the STEM Perspective with Focus on Technologies and Engineering in the Czech Republic, Poland and Slovakia

The curriculum naturally changes as the world changes. The shape of education responds to social need changes, rapid technological developments, and changes in nature, especially in the context of global warming. On the other hand, cultural frameworks and practices, which make national curricula specific, have a stabilizing function. In the Czech Republic, work is currently underway to innovate a key national curriculum document that specifies what, to what extent, and under what conditions pupils learn in primary schools. To reflect on the new competencies, the new curriculum, and its organization based on different views and experiences from abroad, this comparative study was carried out to compare the curricula in the Czech Republic, Slovakia, and Poland. We did not focus on STEM education as a whole, but only on the field of technology and engineering. Thus, we obtained an answer to the question of whether engineering education is implemented in primary schools in these countries. The aim was not only to compare content but to bring a deeper understanding of the subject matter. To this end, we established 6 criteria based on which we made the comparison. Finally, based on the results of the analysis, we discussed the barriers to the implementation of STEM concepts in education in the Czech Republic and drew negative consequences that could be manifested in the case of ill-considered interventions in the national curriculum.
Mobile augmented reality system for object detection, alert, and safety

The versatility and performance of mobile devices has increased greatly in recent years, allowing users to perform more tasks in daily life.While mobile devices and applications provide many benefits for users, some of the most significant are locationbased, including point-of-use tools, navigation, and alert systems.This paper presents a prototype of a cross-platform mobile augmented reality (AR) application with the core purpose of finding a better means to keep a campus community secure and connected.This mobile AR application consists of four core functionalities -an events system, a policing system, a directory system, and a notification system.The events system keeps the community up to date on events that are happening or will soon be happening on campus.The policing system allows the community to stay in arms reach of campus resources that help them to stay secure.The directory system serves as a one-stop shop for campus resources, ensuring that staff, faculty, and students will have a convenient and efficient means of accessing pertinent information on the campus departments.This paper demonstrates how augmented reality (AR) visualizations could be used to supplement existing emergency communications for alert and safety in built spaces.This work highlights the ability to provide evacuation information in a multileveled space in 3D form using AR technology.Specifically, the paper describes the design and implementation of the proposed mobile AR application and reports the results of the pilot study conducted to evaluate perceived ease-of-use and usability.results from this pilot study show that the mobile AR application was helpful in identifying nearest exits, is relatively easy to use, and is useful for navigation and evacuation.
What Is Digital Pedagogy?

Digital pedagogy encompasses teaching and learning in online, hybrid, and face-to-face environments and digital technologies are positioned to serve as vital drivers in the transformation and reform of education. Digital pedagogy is a relatively new academic discipline that deals with teaching and learning and it has undergone rapid development in recent years. This chapter deals with various approaches to defining digital and critical digital pedagogy. Furthermore, it discusses what digital pedagogy entails, its relationship with ODE, the importance of digital transformation in education, and critical digital pedagogy.