Reviewerly/exHarmony huggingface dataset:
КНИГА У СТРУКТУРІ ІНФОРМАЦІЙНОГО ПРОСТОРУ ОСВІТИ

The 21st century will go down in history as the century of electronic (digital, computer) technologies, which radically change the way of life of people and society, which significantly affects all spheres of life - production and thinking, activity and communication, the sphere of science and culture, people's worldview, their preparation for life and work, on the means of education and training. Their rapid development has reached a limit that allows us to assert that today none of the spheres, any type or direction of human activity can function, let alone develop, without the use of digital technologies, computerization. The formation of electronic civilization, the inevitability of the total computerization of life, sets the task of preparing a person for the fluent command of electronic computing equipment and technologies, the competencies of their application in practice. This is done mainly through education and training, which must change its contours, content, and educational technologies. Responding to the challenges of digital civilization, education should form a computerized environment, the development of which should be carried out from the first steps of the child in the educational process, systematically, with a gradual and consistent increase of the load from the zero mark to the peaks of the latest (electronic) cognitive process. Enthusiastic about the prospects of e-learning, some propose to throw out the so-called "outdated material" from the educational process, to eliminate "old means of learning", to unconditionally destroy everything that supposedly no longer meets the challenges of today. The book is now being subjected to special attacks by supporters of the "radical renewal" of education. "Removal" of the book from education is considered by some of them as the main principle and requirement of updating. I am convinced that such a proposal has nothing to do with the real renewal of the educational process. Destroying is easy, creating is difficult. Meanwhile, attacks on traditional learning tools, methods, and technologies not only continue, but seem to be intensifying.
Causal Influence between the JSX Composite Index and the FTSE Straits Times Index in Time Series

This research analyzes the causal influence between the JSX Composite Index and the FTSE Straits Times Index. The data used in this study is a monthly period time series data. The period for the JCI to be observed is January 2015 to June 2022. The VAR method was used to analyze the causal influence of the two indices. The analysis results show that the appropriate VAR model is VAR ( 1,4,5), RMSE value of 212.15 for the JSX Composite Index and 123.76 for the FTSE Straits Times Index. Based on the analysis results using the VAR model, information is obtained that the two indices influence each other. The value of the JSX Composite Index formed in the current period is controlled by the value of the JSX Composite Index and the FTSE Straits Times Index in the previous period. Likewise, for the FTSE Straits Times Index, the results show that the value of the FTSE Straits Times Index formed in the current period is influenced by the value of the JSX Composite Index and the FTSE Straits Times Index in the previous period.
Comparing the Performance of Different Classification Algorithms for Mapping and Assessing Land Cover Changes in Areas with Surface Mining and Complex Landscape Using Landsat Imagery

In order to conduct an accurate classification of the heterogeneous landscape in Jiului Valley, Romania mining basing, four machine learning algorithms (SVMs) and two common algorithms (MLC and MD) have been compared, using a temporal series of Landsat satellite images from the period 1988-2017. By using independent validation, an accuracy assessment was established together with the analysis of the differences between the classification algorithms used. Although all six algorithms used have shown a high overall accuracy (ranging from 80.29% to 93.14%) and Kappa values (from 0.77 to 0.92), SVM-RBF appears to have a higher overall applicability in describing the spatial distribution and the cover density of each land cover category. Results have indicated a large difference in classification accuracy between the SVM-RBF algorithm and commonly used algorithms, the SVM-RBF algorithms have slightly outperformed the MLC with an overall accuracy of 7.14&amp;ndash;8.86% and by 0.0833&amp;ndash;0.1033 kappa coefficient. On the other hand, the same algorithm have outperformed the MD by and overall accuracy of 9.71&amp;ndash;10.86% and by 0.1133&amp;ndash;0.1267 kappa coefficient. By using SVM-RBF, certain classified maps have been developed and used for assessing changes by post classification comparison. The results have shown an average growth of 6.5% in mined areas over the studied period.
Bridging History with AI: A Comparative Evaluation of GPT- 3.5, GPT-4, and Google-BARD in Predictive Accuracy and Fact- Checking

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence (AI) has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language models (LLMs)&amp;mdash;GPT-3.5, GPT-4, and Google- BARD&amp;mdash;in the context of predicting and verifying historical events based on given data. A novel metric, "Distance to Reality" (DTR), is introduced to assess the models' outputs against established historical facts. The results reveal a substantial potential for AI in historical studies, with GPT-4 demonstrating superior performance. This paper underscores the need for further research into AI's role in enriching our understanding of the past and bridging historical knowledge gaps.
Seeing confusion through a new lens: on the impact of atoms of confusion on novices’ code comprehension

Code comprehension is crucial for software maintenance and evolution, but it can be hindered by tiny code snippets that can confuse the developers, called atoms of confusion. Previous studies investigated how atoms impact code comprehension through the perspectives of time, accuracy, and opinions of developers. However, we need more studies evaluating other perspectives and the combination of these perspectives on a common ground through experiments. In our study, we evaluate how the eye tracking method can be used to gain new insights when we compare programs obfuscated by the atoms with functionally equivalent clarified versions. We conduct a controlled experiment with 32 novices in Python and measure their time, number of attempts, and visual effort with eye tracking through fixation duration, fixations count, and regressions count. We also conduct interviews and investigate the subjects’ difficulties with the programs. In our results, the clarified version of the code with Operator Precedence reduced the time spent in the region that contains the atom to the extent of 38.6%, and the number of answer attempts by 28%. Most subjects found the obfuscated version more difficult to solve than the clarified one, and they reported the order of precedence to be difficult to validate. By analyzing their visual effort, in the obfuscated version, we observed an increase of 47.3% in the horizontal regressions count in the atom region, making its reading more difficult. The additional atoms evaluated revealed other interesting nuances. Based on our findings, we encourage researchers to consider eye tracking combined with other perspectives to evaluate atoms of confusion and educators to favor patterns that do not impact the understanding and visual effort of undergraduates.
FPDT: a multi-scale feature pyramidal object detection transformer

Object detection is a fundamental part of autonomous driving algorithms, and with the promotions of transformers in a couple of years, numerous computer vision tasks are integrating transformers into object detectors to acquire a better generalization ability. Building a pure transformer-based detector seems to be a wonderful choice; however, transformers are not omnipotent, and they come with painful drawbacks. Its fundamental operator, multi-head self-attention (MHSA), suffers from the need for computational resources due to its quadratic complexity, which demands an unreasonably high memory usage and critically low throughput. To address this issue, we use a convolution operation to simulate MHSA from transformers by referencing the philosophy and principle of MHSA and making an application migration on convolutional neural networks (CNNs). This gives a detector with power and speed simultaneously. Furthermore, a multi-scale pyramidal feature extractor gives the detector a better view over various scales. In general, our proposed object detector mainly follows the philosophy of attention mechanism, which is implemented by a multi-scale feature pyramidal CNN encoder that simulates the transformer, and a real transformer query neck to extract all of the objects once and, eventually, feed them to the output heads. After training on the COCO2017 dataset, by combining the construction philosophy of the object detector and the philosophy and characteristics of the transformer, our FPDT-Tiny gives an average precision (AP) of up to 34.1 in 150 lower epochs, which is 16.0 and 10.8 higher than CNN-based YOLOv3-Base and SSD-300, respectively. Also, the AP given by our FPDT-Small is up to 37.7 under the same epoch, which is 10.4 and 7.9 higher than the transformer-based detector YOLOS-Small and DETR-ResNet-152, respectively, also demonstrating a comparable performance.
Deep Compressed Super-Resolution Imaging with DMD Alignment Error Correction

In the field of compressed imaging, many attempts have been made to use the high-resolution digital micromirror array (DMD) in combination with low-resolution detectors to construct imaging systems by collecting low-resolution compressed data to reconstruct high-resolution images. However, the difficulty of achieving micrometer-level alignment between DMD devices and detectors has resulted in significant reconstruction errors. To address this issue, we proposed a joint input generative adversarial network with an error correction function that simulates the degradation of image quality due to alignment errors, designed an optical imaging system, and incorporated prior imaging system knowledge in the data generation process to improve the training efficiency and reconstruction performance. Our network achieved the ability to reconstruct 4× high-resolution images with different alignment errors and performed outstanding reconstruction in real-world scenes. Compared to existing algorithms, our method had a higher peak signal-to-noise ratio (PSNR) and better visualization results, which demonstrates the feasibility of our approach.
Exploring Factors Affecting Media Information Literacy

&#x0D; This paper is intended to investigate the determinants of media information literacy by examining the skills of students belonging to the University of Jammu in July 2022. The focal point of the study is to identify the predictors of MIL, by knowing the student’s familiarity with the various concepts of MIL, and their level of MIL skills. MIL is studied through a scale having four parameters, with a 0.741 interclass correlation and a total reliability of.81. The majority of respondents assessed poor MIL skills and distinct from the self-reported MIL (W= 2386.5, p&lt;0.001, and rB = 0.8). It was found that information literacy, computer literacy, and freedom of expression are indirectly affecting the MIL, and is mediated by the capacities to create and evaluate information.Nevertheless, it is directly affected by information literacy.
The Application of Think Pair Share to Enhance Vocabulary Mastery

The study's goal was to see if using think-pair-share to enhance students’ vocabulary mastery was beneficial. The pre-experimental approach was used in this study. MA YMPI Rappang's population was in its second year. The total number of pupils in the population was 65 (XI IPA=19, XI IPS 1= 24, XI IPS 2 = 22), and one class was chosen as a sample using cluster random sampling. This class had a total of 24 students (XI IPS 1). The vocabulary test was the tool employed in this study. The findings of this research revealed that the pre-test received a mean score of 25.91, while the post-test received a mean score of 60.00. This revealed that the pre-test and post-test scores differed significantly. The t-test value (15.112) was higher than the t-table value (2.069). This signifies that HO was turned down and H1 was chosen. The study found that teaching vocabulary utilizing the think-pair-share method helps improve students' vocabulary knowledge among MA YMPI Rappang second-year students.
Multi-target Decision Making Under Conditions of Severe Uncertainty

The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targets often differ in their scale of measurement, classically being either purely ordinal or perfectly cardinal. In this paper, we transfer recent developments from abstract decision theory with incomplete preferential and probabilistic information to this multi-target setting and show how – by exploiting the (potentially) partial cardinal and partial probabilistic information – more informative orders for comparing decisions can be given than the Pareto order. We discuss some interesting properties of the proposed orders between decision options and show how they can be concretely computed by linear optimization. We conclude the paper by demonstrating our framework in an artificial (but quite real-world) example in the context of comparing algorithms under different performance measures.
Identity-based ring signature scheme with multi-designated verifiers

As far as we know, there is only one ring signature scheme with multi-designated verifiers in the literature and the scheme is based on the bilinear pairings which need to consume a lot of computation and under the traditional public key infrastructure (PKI). In order to improve computational efficiency and solve the problem of certificate management in PKI, in this paper, we propose the first identity-based ring signature scheme with multi-designated verifiers without pairings. We prove that our novel scheme's unforgeability, anonymity in the random oracle model and indistinguishability in the standard model under the intractability assumption of discrete logarithm problem and decisional Diffie-Hellman problem, and we prove that our novel scheme's non-transferability in the standard model. We compare the new scheme with a previous scheme in terms of computation and communication at last. The results show that our new scheme is more efficient than the previous scheme and is more suitable for the multi-user setting.
Database Management Education in MYSQL

The purpose of this research is to find out what is database management in MySQL or read My Sequel is a Database Management System often abbreviated as DBMS, MySQL is an open-source database management system. MySQL can be used to create and manage databases and their contents, from the smallest to the very large and to convey information to its users. MySQL is also included in the RDBMS or Relational Database Management system, where in its database structure so that when the data retrieval process uses the relational database method and becomes a liaison between the software and the database server. The thing to remember is that MySQL can be used for free.
Excitation-damping quantum channels

Abstract We study a class of quantum channels describing a quantum system, split into the direct sum of an excited and a ground sector, undergoing a one-way transfer of population from the former to the latter; this construction, which provides a generalization of the amplitude-damping qubit channel, can be regarded as a way to upgrade a trace non-increasing quantum operation, defined on the excited sector, to a possibly trace preserving operation on a larger Hilbert space. We provide necessary and sufficient conditions for the complete positivity of such channels, and we also show that complete positivity can be equivalent to simple positivity when the ground sector is one-dimensional. Finally, we examine the time-dependent scenario and characterize all CP-divisible channels and Markovian semigroups belonging to this class, thus providing a general recipe, beyond the Markovian scenario, to promote a given decay process to a legitimate quantum process on a larger space.
Collaboration between instructional designers and subject matter experts in digital transformation projects

The rapid adoption of online learning during the Covid-19 pandemic and digital transformation associated with new tools and technologies has driven the demand for interprofessional collaboration within the education sector.It is now commonplace for Instructional Designers (IDs) and Learning Technologists (LTs) to work alongside Subject Matter Experts (SMEs) when creating online learning content.Although the professional practice of an ID may vary, the collaborative approach to working with SMEs is similar across sectors.Typically, the role of the SME is to impart expert domain knowledge, skills or behaviours.An ID captures this information and creates a learning experience using pedagogical expertise and design skills.This relationship is not without friction and can be complex and challenging to navigate.Research often focuses on the experiences of SMEs and suggests a simple resolution to avoid conflict; clearly define each agent's role in writing.However, this is not a clear-cut solution.Detailed guidance for IDs is lacking, and conflicts still exist.This study aimed to gain a deeper insight into the interprofessional working relationship between IDs and SMEs.A series of semi-structured interviews were conducted, which looked through the lens of relational working theory and focused on the perspective of the ID.Key themes were identified and aligned with the core concepts of the relational working framework.Findings show that a communication breakdown can negatively impact a project, resulting in power struggles and the delivery of subpar
The impact of digital transformation on academic work

In recent years, the digital transformation of Higher Education (HE) has had a significant impact on educators and teaching practices across the sector.New ways of working have led to teachers experiencing more pressure to develop technical expertise while simultaneously supporting students through a labyrinth of change.This small-scale explorative study investigates educators' experiences of working and teaching online in a teaching-focused institution through the lens of Leavitt's Diamond Model (1964).The data collected consists of six semi-structured interviews with teachers working in a teaching-focused university.The study indicates that many participants have experienced a significant increase in workload since the Covid-19 pandemic, with some exhibiting signs of technostress associated with online working (Panisoara et al., 2021).This seemed especially prevalent with more senior academics as policies and digital infrastructure appear to have led to additional administrative burdens.Despite these findings, the research suggests that Information Communication Technology (ICT) may not be the sole cause for this, but rather, it is a symptom of a more complex phenomenon.It has been further suggested that as the HE landscape evolved, little emphasis has been placed on how organisational initiatives influenced the structure, work tasks, technology, and teachers' experiences.Due to the limited scope of this study, it is unclear if this is an isolated situation within the school or a more extensive sector-wide issue.Nevertheless,
Development of Verified Innovation Process for Healthcare Solutions (VIPHS): A Stepwise Model for Digital Health

Many digital health projects often stop in the pilot or test phase. Realisation of new digital health services is often challenging due to lack of guidelines for the step-by-step roll-out and implementation of the systems when changing work processes and procedures are needed. This study describes development of the Verified Innovation Process for Healthcare Solutions (VIPHS) - a stepwise model for digital health innovation and utilisation using service design principles. A multiple case study (two cases) involving participant observation, role play, and semi-structured interviews were conducted for the model development in prehospital settings. The model might be helpful to support realisation of innovative digital health projects in a holistic, disciplined, and strategic way.
Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives

In the context of medical concept extraction, it is critical to determine if clinical signs or symptoms mentioned in the text were present or absent, experienced by the patient or their relatives. Previous studies have focused on the NLP aspect but not on how to leverage this supplemental information for clinical applications. In this paper, we aim to use the patient similarity networks framework to aggregate different phenotyping modalities. NLP techniques were applied to extract phenotypes and predict their modalities from 5470 narrative reports of 148 patients with ciliopathies (a group of rare diseases). Patient similarities were computed using each modality separately for aggregation and clustering. We found that aggregating negated phenotypes improved patient similarity, but further aggregating relatives' phenotypes worsened the result. We suggest that different modalities of phenotypes can contribute to patient similarity, but they should be aggregated carefully and with appropriate similarity metrics and aggregation models.
Building a Disease Knowledge Graph

Knowledge graphs have proven themselves as a robust tool in clinical applications to aid patient care and help identify treatments for new diseases. They have impacted many information retrieval systems in healthcare. In this study, we construct a disease knowledge graph using Neo4j (a knowledge graph tool) for a disease database to answer complex questions that are time-consuming and labour-intensive to be answered in the previous system. We demonstrate that new information can be inferred in a knowledge graph based on existing semantic relationships between the medical concepts and the ability to perform reasoning in the knowledge graph.
Health Synthetic Data to Enable Health Learning System and Innovation: A Scoping Review

With the recent advancement in the field of machine learning, health synthetic data has become a promising technique to address difficulties with time consumption when accessing and using electronic medical records for research and innovations. However, health synthetic data utility and governance have not been extensively studied. A scoping review was conducted to understand the status of evaluations and governance of health synthetic data following the PRISMA guidelines. The results showed that if synthetic health data are generated via proper methods, the risk of privacy leaks has been low and data quality is comparative to real data. However, the generation of health synthetic data has been generated on a case-by-case basis instead of being scaled up. Furthermore, regulations, ethics, and data sharing of health synthetic data have primarily been inexplicit, although common principles for sharing such data do exist.
Health-Related Content in Transformer-Based Language Models: Exploring Bias in Domain General vs. Domain Specific Training Sets

In this communication, we demonstrate that the bias observed in domain general training sets with health-related content is not improved in domain specific health-communication corpora, contra.