Reviewerly/exHarmony huggingface dataset:
Pedagogical Goal-setting in a Digital Environment: Problem Actualization

During the COVID-19 pandemic (since March 2020) the Faculty of Education at Comenius University in Bratislava immediately switched to distance learning.A part of the university online education was e-testing using the Moodle system.Lecturers had to change their standard approaches, including the introduction of online testing to their teaching.We were interested in the effect of online learning and e-testing on the level of students' knowledge gained in the course "Teaching geometry in primary education".The goal of the study was to investigate the impact of e-learning on knowledge of students of preprimary and primary education.We analyzed the students' results between 2018 and 2021 to compare the traditional teaching and testing in the paper-pencil-compass and ruler form (standard group of students) with the online form of teaching and testing (COVID group of students).Statistical methods of quantitative research were used to analyze the data separately for full-time and part-time students.The statistical analysis showed that there are no differences between the distributions of the percentage scores of standard and COVID group of full-time students.The same result was obtained for the distributions of percentage scores for standard and COVID group of part-time students.Hence, the online form of teaching and testing was equivalent to the in-person form.In addition to the percentage score of students, it is also necessary to investigate other determinants of online education and their impact on the preparation of future primary education teachers.
Improved Methods for Mammogram Breast Cancer Using by Denoising Filtering

In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues with normal dense and the tissues that are cancerous. Therefore, mammography images need to be improved in order to accurately identify and diagnose breast cancer. The most typical goals of images enhancement are to remove noise and improve image details. With the aid of mammography image processing techniques, a special data including distinctive characteristics of tumors can be differentiated, this could help distinguish between malignant and benign cancers. This work focuses on removing noise of pepper &amp; salt, improving image to increase the quality of mammography and enhance early detection of breast cancer. A specific approach is employed to do this, including of two phases of image denoising base filtration and one phase to improve contrast. The stages of filtering contain the using of wiener and median filters. The contrast enhancement stage utilizes (CLAHE) which is an abbreviation for contrast limited adaptive histogram equalization. Evaluating the performance is done via contrast histogram for the CLAHE and MSE &amp; PSNF for the filters. The results demonstrate that the work technique is doing better when put to comparison with other approaches in term of low MSE (1.1645) and high PSNR (47.4750). The technique will be assessed with additional kinds of noise for future work.
Research on Voice-Driven Facial Expression Film and Television Animation Based on Compromised Node Detection in Wireless Sensor Networks

With the continuous development of social economy, film and television animation, as the spiritual needs of ordinary people, is more and more popular. Especially for the development of emerging technologies, the corresponding voice can be used to change AI expression. But at the same time, how to ensure the synchronization of language sound and facial expression is one of the difficulties in animation transformation. Relying on the compromised node detection of wireless sensor networks, this paper combs the synchronous traffic flow between the speech signals and facial expressions, finds the pattern distribution of facial motion based on unsupervised classification, realizes training and learning through neural networks, and realizes one-to-one mapping to facial expressions by using the rhyme distribution of speech features. It avoids the defect of robustness of speech recognition, improves the learning ability of speech recognition, and realizes the driving analysis of facial expression film and television animation. The simulation results show that the compromised node detection in wireless sensor networks is effective and can support the analysis and research of speech-driven facial expression film and television animation.
Methods of improving the system of motivation of the university teaching staff

The purpose of the article is a systematic scientific study of the most optimal methods for motivating personnel of higher educational institutions and their improvement, taking into account the economic state of the country and the specifics of the processes of social transformation, which formed the basis for the development of methodological and practical recommendations. The main research methods were information retrieval methods, universal scientific methods, including methods of comparative and factor analysis, detailing, synthesis, generalization. This publication examines the methods of motivation and incentives for the teaching staff of universities, which are in demand for use in the transition to the forms of blended and e-learning, along with the use of traditional approaches; a method for calculating the fund of surcharges, allowances and other incentive payments to the teaching staff is proposed, reflecting the relationship and mutual influence of workload, remuneration and surcharges, an objective assessment of work with the volume of work actually performed, overtime work, scientific work, the introduction of modern methods and technologies of teaching allows you to optimally distribute funds to motivate the teaching staff. The materials of the article will be of interest to scientific and practical workers, graduate students and students.
All You Need Is a Few Dots to Label CT Images for Organ Segmentation

Image segmentation is used to analyze medical images quantitatively for diagnosis and treatment planning. Since manual segmentation requires considerable time and effort from experts, research to automatically perform segmentation is in progress. Recent studies using deep learning have improved performance but need many labeled data. Although there are public datasets for research, manual labeling is required in an area where labeling is not performed to train a model. We propose a deep-learning-based tool that can easily create training data to alleviate this inconvenience. The proposed tool receives a CT image and the pixels of organs the user wants to segment as inputs and extract the features of the CT image using a deep learning network. Then, pixels that have similar features are classified to the identical organ. The advantage of the proposed tool is that it can be trained with a small number of labeled data. After training with 25 labeled CT images, our tool shows competitive results when it is compared to the state-of-the-art segmentation algorithms, such as UNet and DeepNetV3.
A Comprehensive Comparative Evaluation of Machine Learning Algorithms on Facebook Comment Dataset

Data mining is an emerging technique with its application in various areas such as health care, education, travel, social media, and banking. The data can be either labeled or unlabeled. When it comes to social media, the various platforms generate an infinite amount of data. This data can be of immense importance as a lot of hidden information can be discovered after data mining. In this paper, machine-learning algorithms such as Decision Trees, SVM, and Linear Regression and their variants are applied on the Facebook comment dataset, obtained from the UCI machine learning repository. The dataset has 40,949 instances and 54 attributes. The goal is to predict the number of comments a Facebook post will get based on various conditions. The results indicate that the Fine Gaussian SVM variation of SVM yielded the highest prediction accuracy. The evaluation was done on different parameters such as average testing accuracy (%), Root Mean Square Error (RMSE), R- Squared, Mean Square Error (MSE), Mean Absolute Error (MAE), prediction speed (Obs/sec), and training time (Machine cycle). It is concluded that SVM is an ideal choice to solve prediction problems associated with social media data.
Two-Stage Deep Anomaly Detection With Heterogeneous Time Series Data

We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line. Given <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> time series data consisting of operation cycle signals and sensor signals, we aim at discovering abnormal events. Motivated by our empirical findings that conventional single-stage benchmark approaches may not exhibit satisfactory performance under our challenging circumstances, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-stage</i> deep anomaly detection (T-DAD) framework in which two different unsupervised learning models are adopted depending on types of signals. In Stage I, we select anomaly candidates by using a model trained by operation cycle signals; in Stage II, we finally detect abnormal events out of the candidates by using another model, which is suitable for taking advantage of temporal continuity, trained by sensor signals. A distinguishable feature of our framework is that operation cycle signals are exploited first to find likely anomalous points, whereas sensor signals are leveraged to filter out unlikely anomalous points afterward. Our experiments comprehensively demonstrate the superiority over single-stage benchmark approaches, the model-agnostic property, and the robustness to difficult situations.
Analysis of Trends for Weapon System Accidents Using Social Network Analysis

Since military weapon accidents or breakdowns are directly linked to enormous damage, it is important to analyze the causes of weapons system accidents. Recently, in the defense sector, there have been cases in which budget has been saved through analysis of the causes of frequent breakdowns and improvement activities that have occurred in the process of operating weapon systems since 2015. But due to the nature of the defense sector, it is not easy to collect data and studies on weapons system accidents have been insufficient so far. Therefore, this study aims to investigate the causes and types of military weapon accidents by collecting military weapon accident data for military weapon systems and analyzing trends by weapon system classification through the analysis process. It analyzes statistically and visually through social network analysis, NodeXL. It is expected that this study will help improve the stability of the weapon system by reducing the number of military weapon accidents and failures.
Neural Network Approaches to Point Lattice Decoding

We characterize the complexity of the lattice decoding problem from a neural network perspective.The notion of Voronoi-reduced basis is introduced to restrict the space of solutions to a binary set.On the one hand, this problem is shown to be equivalent to computing a continuous piecewise linear (CPWL) function restricted to the fundamental parallelotope.On the other hand, it is known that any function computed by a ReLU feed-forward neural network is CPWL.As a result, we count the number of affine pieces in the CPWL decoding function to characterize the complexity of the decoding problem.It is exponential in the space dimension n, which induces shallow neural networks of exponential size.For structured lattices we show that folding, a technique equivalent to using a deep neural network, enables to reduce this complexity from exponential in n to polynomial in n.Regarding unstructured MIMO lattices, in contrary to dense lattices many pieces in the CPWL decoding function can be neglected for quasioptimal decoding on the Gaussian channel.This makes the decoding problem easier and it explains why shallow neural networks of reasonable size are more efficient with this category of lattices (in low to moderate dimensions).
Horizon Targeted Loss-Based Diverse Realistic Marine Image Generation Method Using a Multimodal Style Transfer Network for Training Autonomous Vessels

Studies on virtual-to-realistic image style transfer have been conducted to minimize the difference between virtual simulators and real-world environments and improve the training of artificial intelligence (AI)-based autonomous driving models using virtual simulators. However, when applying an image style transfer network architecture that achieves good performance using land-based data for autonomous vehicles to marine data for autonomous vessels, structures such as horizon lines and autonomous vessel shapes often lose their structural consistency. Marine data exhibit substantial environmental complexity, which depends on the size, position, and direction of the vessels because there are no lanes such as those for cars, and the colors of the sky and ocean are similar. To overcome these limitations, we propose a virtual-to-realistic marine image style transfer method using horizon-targeted loss for marine data. Horizon-targeted loss helps distinguish the structure of the horizon within the input and output images by comparing the segmented shape. Additionally, the design of the proposed network architecture involves a one-to-many style mapping technique, which is based on the multimodal style transfer method to generate marine images of diverse styles using a single network. Experiments demonstrate that the proposed method preserves the structural shapes on the horizon more accurately than existing algorithms. Moreover, the object detection accuracy using various augmented training data was higher than that observed in the case of training using only virtual data. The proposed method allows us to generate realistic data to train AI models of vision-based autonomous vessels by actualizing and augmenting virtual images acquired from virtual autonomous vessel simulators.
Detection Datasets: Forged Characters for Passport and Driving Licence

Forged characters detection from personal documents including a passport or a driving licence is an extremely important and challenging task in digital image forensics, as forged information on personal documents can be used for fraud purposes including theft, robbery etc. For any detection task i.e. forged character detection, deep learning models are data hungry and getting the forged characters dataset for personal documents is very difficult due to various reasons, including information privacy, unlabeled data or existing work is evaluated on private datasets with limited access and getting data labelled is another big challenge. To address these issues, we propose a new algorithm that generates two new datasets named forged characters detection on passport (FCD-P) and forged characters detection on driving licence (FCD-D). To the best of our knowledge, we are the first to release these datasets. The proposed algorithm first reads the plain image, then performs forging tasks i.e. randomly changes the position of the random character or randomly adds little noise. At the same time, the algorithm also records the bounding boxes of the forged characters. To meet real world situations, we perform multiple data augmentation on cards very carefully. Overall, each dataset consists of 15000 images, each image with size of 950 x 550. Our algorithm code, FCD-P and FCD-D are publicly available.
Carré Phase Shifting Algorithm for Wavelength Scanning Interferometry

Wavelength scanning interferometry is an interferometric technique for measuring surface topography without the well-known 2π phase ambiguity limitation. The measurement accuracy and resolution of this technique depends, among other factors, on the algorithm used to evaluate its sinusoidal interference pattern. The widely used fast Fourier transform analysis experiences problems such as waviness error across the measured surface due to spectral leakage. This paper introduces a new fringe analysis method based on the Carré phase shifting algorithm combined with a least squares fitting approach. Numerical simulation was carried out to assess the performance of the Carré algorithm in comparison to fast Fourier transform analysis, and the same was validated by presenting four experimental case study examples (a surface flat, a ceramic ball bearing, a flexible thin film, and a discontinuous step height sample). The analysis results show that the proposed Carré algorithm with least squares fitting can significantly eliminate the waviness error, especially when measuring steep surfaces.
Communicative competence of elementary school teachers

The article considers the problem of teacher's professional communication through the prism of his/her communicative competence formation. The authors present their own approach to the essence of the concept “communicative competence”. The value of personal qualities of the teacher in establishing communicative relations in the system “teacher-student” and professional development was found out. The aim was to determine the features of the formation of communicative qualities of the elementary school teacher, the application in practical activity; the main conditions and techniques of development of successful pedagogical communication; the characteristics of the structure of communicative competence. To disclose the issue of communicative competence we used general scientific methods (analysis, synthesis, comparison), interdisciplinary (interview, conversation, observation) and methods of mathematical statistics. In order to empirically study the process of formation of communicative competence of a teacher, we conducted research among students who had experience of practical classes in elementary school. The sample consisted of 200 applicants for the specialty “Elementary education”. The results showed that the dominant majority of the respondents have no difficulties in professional communication. They are able to provide students with the necessary instructional material and establish a trusting relationship with them. However, the presence of students insecure about their own communicative competence indicates the need to introduce new approaches and methods of practical training of future specialists into the educational space of higher education.
<b>tlrmvnmvt</b>: Computing High-Dimensional Multivariate Normal and Student- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:mi>t</mml:mi></mml:mrow> </mml:math> Probabilities with Low-Rank Methods in <i>R</i>

This paper introduces the usage and performance of the R package tlrmvnmvt, aimed at computing high-dimensional multivariate normal and Student-t probabilities. The package implements the tile-low-rank methods with block reordering and the separationof-variable methods with univariate reordering. The performance is compared with two other state-of-the-art R packages, namely the mvtnorm and the TruncatedNormal packages. Our package has the best scalability and is likely to be the only option in thousands of dimensions. However, for applications with high accuracy requirements, the TruncatedNormal package is more suitable. As an application example, we show that the excursion sets of a latent Gaussian random field can be computed with the tlrmvnmvt package without any model approximation and hence, the accuracy of the produced excursion sets is improved.
THE IMPACT OF STAKEHOLDERS IN AGILE SOFTWARE DEVELOPMEN

The market for software development projects is dynamic and complex. The stakeholders involved in the software development process come from different organisational positions and therefore have varying interests and expectations from the product. Effective project management practices enable successful on-time delivery of high-quality product. It is essential to identify the stakeholder roles and responsibilities to effectively manage the development practices within the organisation. Elucidating the stakeholder roles will enable stakeholder management via effective communication and sustainable practices that reflect stakeholder interests. This study elucidates the roles and responsibilities of the stakeholders associated with Agile software development projects. The study identifies that for effective stakeholder management practices understanding of the stakeholder roles and interests is vital. Additionally, application of Agile software development methodology would result in successful development of high-quality software solutions.
Analisis Peran Teknologi Digital Sebagai Solusi Problematika Belajar Online yang Berkelanjutan

Since the COVID-19 pandemic struck Indonesia, there have been many changes that have affected various aspects of life, including education. The difficulties, among others, are the difficulty of getting materials, this makes the use of technology very much needed. This journal aims to review the important role of educational technology as a solution to learning problems during the pandemic, using a descriptive-analytical literature study method. The results of this review indicate that digital technology has an important role in the teaching and learning process during the current pandemic. Submission of material is also done using technology. In carrying out something, there are always obstacles and obstacles to be overcome, including those related to values, knowledge, attitudes, skills, and facilities and infrastructure used when learning online. Sufficient training and literacy can be a solution to face these challenges. Utilization of digital-based technology is considered effective and becomes a teacher's solution in carrying out online learning. The hope is that this digital technology can continue to be used as a sustainable medium.
Essential Components of Software Framework for Mobile Forensic Tool to Extract and Decode the Mobile Artifacts

In the today’s era, the illegal activities, especially crimes committed through mobile devices, have increased drastically. Mainly the advancement in the mobile technology is the main reason behind this. Hence to catch the cyber criminals who committed crime through mobile devices, the Mobile Forensic comes into picture. There are many mobile forensic tools in the market available in the market in which some tools are freely available where some are paid one. These tools are also in the evolution phase continuously but the challenges in the mobile forensics due to leap and bound development in the mobile technology are still unaddressed. The main reason behind the failure of the mobile forensic software lies in their software framework. This paper focuses on the essential components which must be a part of the software framework of the mobile forensic tools so that they can march towards becoming more sophisticated, universal and versatile too. Also this work emphasizes on the need of new component called Evidence Integrity Protection Component which is missing in the latest tools so far.