Reviewerly/exHarmony huggingface dataset:
The Educational University Policy as the Most Important Means of Implementing the National Education Concept

The global changes that the modern world is facing are distinguished by their constancy and continuity, and, moreover, are characterized by a clearly expressed tendency to accelerate. Changes of this magnitude cannot but affect the participants in these processes, and the role of education in human activity is becoming increasingly important. On the other hand, young people are becoming an increasingly active participant in the social, political and cultural life of the state, which requires new theoretical and activity approaches and systematic mental and activity methods in working with youth, as well as the development of new educational content for those areas of training that prepare personnel for the youth sector. As a result, the problem of the system of organization of educational work in the system of higher education comes to the fore and becomes more relevant than ever. Educational work should be carried out along two vectors — the preparation of future youth leaders who will implement youth policy at the municipal, regional and federal levels, and the education of civic responsibility in all future specialists.
A Survey Study of Students’ Motivation in English Language Learning

The purpose of this paper was to determine (1) the degree of students' motivation in English language learning and (2) the dominating motive between instrumental and integrative motivation in English language learning. This study was carried out at SMAN 1 Palopo. Mixed technique, which is mostly quantitative, was used, with a survey using an online Likert scale questionnaire administered to 31 respondents and five of them interviewed. The data has been evaluated quantitatively and statistically using the paired sample t-test and qualitative description. The questionnaire was examined according to the proportion of each response. To convert questionnaire data into percentages, the researcher multiplied the number of occurrences by 100 percent and then divided by the total number of respondents. The findings of this study reveal that SMAN 1 Palopo students have strong instrumental and integrative motivation, with a mean score of 4.12, when it comes to studying English. According to the results of the questionnaire survey, students' responses of agreement and strong agreement predominated, whereas responses of disagreement and severe disagreement did not occur considerably. The overall findings of the study indicate that students' English-learning motivation is mostly integrative. This indicates that students study English based on their initiative and desire to grasp and learn more about English culture.
Looking into the past: Eye-tracking mental simulation in physical inference

Mental simulation is a powerful cognitive capacity that underlies people's ability to draw inferences about what happened in the past from the present. Recent work suggests that eyetracking can be used as a window through which one can study the process of mental simulation in intuitive physics tasks. In our experiment, participants have to figure out in which of three holes a ball was dropped in a virtual Plinko box. We develop a computational model of human intuitive physical reasoning in Plinko that runs repeated simulations in a noisy physics simulator in order to infer in which hole the ball was dropped. We evaluate our model's behavior against multiple human data signals: trial judgments, response times, and eye-movement data. We find that a model that sequentially samples simulations while balancing uncertainty and reward best explains the patterns of participant behavior we observe in these three signals.
Modern Visualization Means of Educational Information While Teaching Foreign Language at a University

he article contains the description of various ways of visualizing educational information as a prerequisite for successful foreign language teaching. The data contained in the article are based on the analysis of digital visualization services of educational content, on the results of studying the methodological foundations of using various information visualization tools at various stages of foreign language teaching. The article highlights in detail the most modern ways and means of visualizing information, including mental maps, time scales, etc.
Discovering the Need for Information Assurance to Assure the End Users: Methodologies and Best Practices

The use of software to support the information infrastructure that governments, critical infrastructure providers and businesses worldwide rely on for their daily operations and business processes is gradually becoming unavoidable. Commercial off-the shelf software is widely and increasingly used by these organizations to automate processes with information technology. That notwithstanding, cyber-attacks are becoming stealthier and more sophisticated, which has led to a complex and dynamic risk environment for IT-based operations which users are working to better understand and manage. This has made users become increasingly concerned about the integrity, security and reliability of commercial software. To meet up with these concerns and meet customer requirements, vendors have undertaken significant efforts to reduce vulnerabilities, improve resistance to attack and protect the integrity of the products they sell. These efforts are often referred to as “software assurance.” Software assurance is becoming very important for organizations critical to public safety and economic and national security. These users require a high level of confidence that commercial software is as secure as possible, something only achieved when software is created using best practices for secure software development. Therefore, in this paper, we explore the need for information assurance and its importance for both organizations and end users, methodologies and best practices for software security and information assurance, and we also conducted a survey to understand end users’ opinions on the methodologies researched in this paper and their impact.
THE MORPHO-SYNTACTIC ASPECT OF MEDIA POLITICAL DISCOURSE

Manipulation through different media platforms can immediately span practically every element of human life since it attracts, maintains and processes human attention very quickly. This unparalleled technical potential for propaganda and manipulation on social networks can be realized through different language means that may be utilized to influence others. In this paper, our main task will be to find out how manipulation in media is realized through the morpho-syntactic properties of language.
Internet of Things and Blockchain-Based Framework for Coronavirus (COVID-19) Disease

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient's condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.
FASSVid: Fast and Accurate Semantic Segmentation for Video Sequences

Most of the methods for real-time semantic segmentation do not take into account temporal information when working with video sequences. This is counter-intuitive in real-world scenarios where the main application of such methods is, precisely, being able to process frame sequences as quickly and accurately as possible. In this paper, we address this problem by exploiting the temporal information provided by previous frames of the video stream. Our method leverages a previous input frame as well as the previous output of the network to enhance the prediction accuracy of the current input frame. We develop a module that obtains feature maps rich in change information. Additionally, we incorporate the previous output of the network into all the decoder stages as a way of increasing the attention given to relevant features. Finally, to properly train and evaluate our methods, we introduce CityscapesVid, a dataset specifically designed to benchmark semantic video segmentation networks. Our proposed network, entitled FASSVid improves the mIoU accuracy performance over a standard non-sequential baseline model. Moreover, FASSVid obtains state-of-the-art inference speed and competitive mIoU results compared to other state-of-the-art lightweight networks, with significantly lower number of computations. Specifically, we obtain 71% of mIoU in our CityscapesVid dataset, running at 114.9 FPS on a single NVIDIA GTX 1080Ti and 31 FPS on the NVIDIA Jetson Nano embedded board with images of size 1024×2048 and 512×1024, respectively.
Despeckling of Ultrasound Images Using Block Matching and SVD in Sparse Representation

This work proposes a novel scheme for speckle suppression on medical images acquired by ultrasound sensors. The proposed method is based on the block matching procedure by using mutual information as a similarity measure in grouping patches in a clustered area, originating a new despeckling method that integrates the statistical properties of an image and its texture for creating 3D groups in the BM3D scheme. For this purpose, the segmentation of ultrasound images is carried out considering superpixels and a variation of the local binary patterns algorithm to improve the performance of the block matching procedure. The 3D groups are modeled in terms of grouped tensors and despekled with singular value decomposition. Moreover, a variant of the bilateral filter is used as a post-processing step to recover and enhance edges' quality. Experimental results have demonstrated that the designed framework guarantees a good despeckling performance in ultrasound images according to the objective quality criteria commonly used in literature and via visual perception.
The Matter of Teaching Skimming in Foreign Electronic Sources of Information

The article actualizes the topic of skimming in foreign language. The author describes the characteristics of skimming and notes the main problems faced by first- and second-year students of a non-linguistic university when reading linear or non-linear electronic text materials in a foreign language for skimming. The author describes the features of reading in an electronic foreign language environment. The article presents a comparison of reading electronic sources of information in a foreign language with reading traditional paper sources. The reasons for possible cognitive overload when working with electronic texts in a digital environment are described. The author suggests an algorithm of actions to help foreign language learners overcome possible difficulties during skimming.
Fast cross-staining alignment of gigapixel whole slide images with application to prostate cancer and breast cancer analysis

Joint analysis of multiple protein expressions and tissue morphology patterns is important for disease diagnosis, treatment planning, and drug development, requiring cross-staining alignment of multiple immunohistochemical and histopathological slides. However, cross-staining alignment of enormous gigapixel whole slide images (WSIs) at single cell precision is difficult. Apart from gigantic data dimensions of WSIs, there are large variations on the cell appearance and tissue morphology across different staining together with morphological deformations caused by slide preparation. The goal of this study is to build an image registration framework for cross-staining alignment of gigapixel WSIs of histopathological and immunohistochemical microscopic slides and assess its clinical applicability. To the authors' best knowledge, this is the first study to perform real time fully automatic cross staining alignment of WSIs with 40× and 20× objective magnification. The proposed WSI registration framework consists of a rapid global image registration module, a real time interactive field of view (FOV) localization model and a real time propagated multi-level image registration module. In this study, the proposed method is evaluated on two kinds of cancer datasets from two hospitals using different digital scanners, including a dual staining breast cancer data set with 43 hematoxylin and eosin (H&E) WSIs and 43 immunohistochemical (IHC) CK(AE1/AE3) WSIs, and a triple staining prostate cancer data set containing 30 H&E WSIs, 30 IHC CK18 WSIs, and 30 IHC HMCK WSIs. In evaluation, the registration performance is measured by not only registration accuracy but also computational time. The results show that the proposed method achieves high accuracy of 0.833 ± 0.0674 for the triple-staining prostate cancer data set and 0.931 ± 0.0455 for the dual-staining breast cancer data set, respectively, and takes only 4.34 s per WSI registration on average. In addition, for 30.23% data, the proposed method takes less than 1 s for WSI registration. In comparison with the benchmark methods, the proposed method demonstrates superior performance in registration accuracy and computational time, which has great potentials for assisting medical doctors to identify cancerous tissues and determine the cancer stage in clinical practice.
Image Target Detection and Recognition Method Using Deep Learning

Image target detection and recognition had been widely used in many fields. However, the existing methods had poor robustness; they not only had high error rate of target recognition but also had high dependence on parameters, so they were limited in application. Therefore, this paper proposed an image target detection and recognition method based on the improved R-CNN model, so as to detect and recognize the dynamic image target in real time. Based on the analysis of the existing theories of deep learning detection and recognition, this paper summarized the composition and working principle of the traditional image target detection and recognition system and compared the basic models of target detection and recognition, such as R-CNN network, Fast-RCNN network, and Faster-RCNN network. In order to improve the accuracy and real-time performance of the model in image target detection and recognition, this paper adopted the target feature matching module in the existing R-CNN network model, so as to obtain the feature map close to the same target through similarity calculation for the features extracted by the model. Therefore, an image target detection and recognition algorithm based on the improved R-CNN network model is proposed. Finally, the experimental results showed that the image target detection and recognition algorithm proposed in this paper can be better applied to image target detection and classification in complex environment and had higher detection efficiency and recognition accuracy than the existing models. The target detection and recognition algorithm proposed in this paper had certain reference value and guiding significance for further application research in related fields.
Formalising Fisher's Inequality: Formal Linear Algebraic Proof Techniques in Combinatorics

The formalisation of mathematics is continuing rapidly, however combinatorics continues to present challenges to formalisation efforts, such as its reliance on techniques from a wide range of other fields in mathematics. This paper presents formal linear algebraic techniques for proofs on incidence structures in Isabelle/HOL, and their application to the first formalisation of Fisher's inequality. In addition to formalising incidence matrices and simple techniques for reasoning on linear algebraic representations, the formalisation focuses on the linear algebra bound and rank arguments. These techniques can easily be adapted for future formalisations in combinatorics, as we demonstrate through further application to proofs of variations on Fisher's inequality.
Study of Thermal Behavior in Mixed Scrap Tires (ST) And Polypropylene (PP) Plastic Using Thermogravimetry Analysis (TGA) and Differential Scanning Calorimetry (DSC)

&lt;p&gt;The world's rapid population growth, technological progress, and industrialization lead to an increase in fossil energy demand such as oil, coal, and natural gas. It is feared that this could lead to a scarcity of fossil energy in the future, therefore the use of alternative energy must be considered. One interesting concept to develop is waste to energy by utilizing plastic waste and also tires on vehicles. This is very possible because the two wastes contain elements of hydrocarbons which are also contained in fossil fuels so they can be processed to become alternative energy which can simultaneously reduce the population of the waste due to the increase in the world's population. This study aims to investigate the thermal behavior of scrap tires (ST) and polypropylene (PP) plastic using thermogravimetry analysis (TGA) and differential scanning calorimetry (DSC). TGA thermogram showed that the feedstock samples were degraded in the temperature range of 331,97 &lt;sup&gt;o&lt;/sup&gt;C to 448,08&lt;sup&gt; o&lt;/sup&gt;C. The interpretation of the DSC thermogram that showing information about the glass transition point and the melting point of the feedstock polymer will also be presented in this study. &lt;em&gt;&lt;/em&gt;&lt;/p&gt;
PERFORMANCE ANALYSIS OF CAPTCHA BASED BLOCKING AND RESPONSE OF AN INTRUSION DETECTION MODEL USING SIGNATURE

Intrusion Detection System is the process of intelligently monitoring the events occurring in a computer system or network, analyzing them for signs of violations of a security policy. Its primary aim is to protect the availability, confidentiality and integrity of critical networked information systems. This paper considered and reviewed a CAPTCHA based intrusion detection model. A method of incorporating signature was used along with the CAPTCHA in the intrusion detection model to clear the controversy identified in the existing model. The signature provides a means of identifying intruders that are able to by-pass the system as legitimate users. The model was implemented using a website hosted online. Dataset obtained from the site was analyzed based on Naïve Bayes classification model using confusion matrices. Implementation of the data analysis was carried out using RStudio software package. Analyzed results shows a better Detection Rate (DR), Accuracy (CR) and False Positive Rate (FPR). This shows that the developed system has significant capability of identifying intelligent spywares targeted at breaking CAPTCHA
Overview of device-independent continuous-variable quantum key distribution

The objects of study of this paper are quantum key distribution (QKD) protocols and systems, in particular, continuous variable (CV) ones with untrusted devices (measurement devices or light sources).The present work is devoted to the consideration of such systems, namely, device-indepentent CV-QKD, and to the discussion of their performance.
AN ANALYSIS OF THE VARIATION IN HUMAN HEIGHT BASED ON PHENOTYPIC TRAITS IN HEREDITY

The phenotypic traits in offspring’s are often dependent on that of the parents. Taking height as the phenotypic trait, we can obtain a regression model for parents and their offspring’s, and also test for the significance of the overall model. As the detection of causal links between genetic and phenotypic variation is accelerating, a re-examination for our conceptual tools may help by finding unifying principles within the swarm of data. The analysis from this research reveal that Multiple Correlation Coefficient is 0.87 which implies that there is a strong positive relationship between Offspring’s Growth and Parent’s Phenotype with R-squared value of 0.75 which implies that 75% of Offspring’s attribute will result from Parent Phenotype. A Multiple Regression model given as offspring’s Growth = 58.589 + 0.535Father’s Height +0.140Mother’s Height.The value 0.535 implies the contribution per unit change of Father’s Height in Offspring’s Growth and 0.140 is the contribution per unit change of Mothers Height in Offspring’s Growth. The Multiple Regression model is statistically significant with p-value of 0.003 less than 0.05. Multicolinearity test in the analysis shows that the predictor variables are moderately Collinear observing the Variance Inflation Factor (VIF). The recommendations require parents to put more efforts on preventing negative phenotypic traits from transferring from the environment to their offspring and couples should try to check their Genotype and Blood group since they highly contribute to the Offspring characteristics
TransOra: A Transaction-preserving and Transparent Distributed Oracle on Permissioned Blockchain For Hybrid Smart Contracts

Hybrid transaction in blockchain enlarges the application domain of smart contracts by enabling off-chain data access. In support of the hybrid transaction, blockchain oracle is an indispensable middleware responsible for receiving data-request tasks and pushing the result back to chain. Though solutions supporting hybrid transactions do exist, they are less featured or applicable for both contract and application developers. On one hand, the racing-condition among several concurrent hybrid transactions should be manually resolved by contract developers. On the other hand, application developers have to associate each individual task with its parent hybrid transaction in order to obtain the result. In this paper, a novel block-chain oracle platform with a set of simple but unified API for both hybrid and traditional transactions is proposed. Firstly, we design and implement a transactional storage layer and a transaction scheduler for transaction preservation and ordering. Secondly, with the help of an automatic result tracing mechanism, application developers are able to acquire transaction results transparently without awareness of oracles and underlying task execution. Experiments are conducted on one of current prevalent permissioned blockchain platforms, RepChain, and the result shows that all of the system design goals are fulfilled with merely an acceptable loss of performance.
Chatbot Menggunakan Natural Language Processing untuk Pembelajaran Bahasa Inggris Berbasis Android

Learning languages nowadays is massive and varies, therefore the interest in learning keeps increasing. But there are some limitations that cause students not to try to learn something, such as the matter of time and the fear of pronouncing and speaking a foreign language whether to people they know or do not know. Therefore, this smart English language learning application is one of the solutions as a platform for learning and practice. This application uses a Natural Language Processing (NLP) method that can act as a friend to practice speaking, especially English. This research is an option for learning languages that is more interactive and fun.