Reviewerly/exHarmony huggingface dataset:
HIGH PEDAGOGICAL SCHOOL AS A FACTOR OF ENVIRONMENT EDUCATION FORMATION IN THE REGION (THE CASE OF INNER ASIA CAPITALS)

The article is devoted to the some aspects of formation of the pedagogical high-er school in Inner Asia capitals: Ulan-Ude city (the Republic of Buryatia, Russia) and Hoh-Hot city (the Inner Mongolia Autonomous region, China). The importance of re-search is obvious, because it is necessary to comprehend the changes happening in the 20th century in educational systems and its influences on modern processes, to analyze and es-tablish the reasons of the transformational processes happening in an education system that is impossible without experience gained from previous generation. Methodological basis of the research is the historical method, and also systematic and structural-functional analysis. Results of the study let us to reveal the features of development and formation of the pedagogical higher school of Inner Asia considering their history and national color and also have a direct relation to one of the most actual problem of cultural, information and educational security. The obtained results can be used in the work of public authorities involved in the development of public policy in the field of education, in reading of the lecture course, special seminars on pedagogy and history of education for the students and specialists of humanities.
Mapping of clay soils exposed to the shrinking - swelling phenomenon, with EO - 1 - Hyperion data in the region of Sidi - Chahmi, Algeria

Satellite images have a remarkable ability to help researchers map the distribution and detect the nature of the soil exposed on the earth's surface. The remote-sensing mapping of clayey soils makes it possible to determine their shrinking-swelling potentials. This approach is helpful in analyzing large areas where the traditional sampling approach with laboratory analysis is expensive. This high cost often leads to a limited number of samples and thus to a poor representation of a site with an underestimation of the presence of swelling soils. The Sidi - Chahmi area is representative of this type of soil. In addition, it is an area that undergoes the substantial urban sprawl of the city of Oran. The urbanization of this area is under the risk of clay-type soil movements, the identification of which by satellite imagery may help mitigate shrink-swell risk. EO-1-Hyperion hyperspectral images, whose effectiveness has already been proven for mapping clay minerals, were used to identify minerals such as illite, montmorillonite, and kaolinite in the Sidi - Chahmi area.
Faceted Classifications as Linked Data: A Logical Analysis

Faceted knowledge organization systems have sophisticated logical structures, making their representation as linked data a demanding task. The term facet is often used in ambiguous ways: while in thesauri facets only work as semantic categories, in classification schemes they also have syntactic functions. The need to convert the Integrative Levels Classification (ILC) into SKOS stimulated a more general analysis of the different kinds of syntactic facets, as can be represented in terms of RDF properties and their respective domain and range. A nomenclature is proposed, distinguishing between common facets, which can be appended to any class, that is, have an unrestricted domain; and special facets, which are exclusive to some class, that is, have a restricted domain. In both cases, foci can be taken from any other class (unrestricted range: free facets), or only from subclasses of an existing class (parallel facets), or be defined specifically for the present class (bound facets). Examples are given of such cases in ILC and in the Dewey Decimal Classification (DDC).
Motion Estimation

Algorithms for aligning images and estimating motion in video sequences are among the most widely used in computer vision. For example, frame-rate image alignment is widely used in digital cameras to implement their image stabilization (IS) feature.
Role of pedagogical design using the ADDIE model in the design of online lesson components

&nbsp; &nbsp; &nbsp; Currently, the issues of digital transformation of education remain relevant, which is associated with the socio-political conditions of life in modern society.During the period of distance learning, the need to create and design video lessons and online lessons in real time increases. Designing these types of activities involves the use of pedagogical design as a process of analyzing learning needs and goals. The aim of the study is to identify the role of pedagogical design using the ADDIE model in the design of components of online lessons in Russian language and literature in a comprehensive school. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The article summarizes the points of view of researchers regarding the term &amp;quot;pedagogical design&amp;quot;, reviews the scientific literature, describes models for designing video and online lessons. On the example of using the model of pedagogical design ADDIE, forms of designing educational material in the Russian language are proposed, built in the form of simulators - interactive tasks with automatic verification of the answer. The value of the study is that the results obtained can be used in the design of video components and online lessons in the Russian language and literature in a comprehensive school. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The authors of the article pay special attention to pedagogical design as a process in the development of technical &nbsp;specifications when designing the content of online lessons, digital content, simulators and educational multimedia tools. Keywords:pedagogical design, ADDIE model, designing online lessons, Russian language and literature &nbsp
Uncovering Complex Relations in Patient Pathways based on Statistics: the Impact of Clinical Actions

Process mining is a family of techniques that can aid healthcare organizations in improving their processes.Most existing process mining techniques do not provide insights into the impact that activities can have on the process.Some novel techniques try to address this issue, but these techniques are either not generic in their approach or cannot provide insights into complex relations in organizational processes.We propose a novel and generic approach with the goal of producing insights into statistical relations within healthcare processes.We apply the approach on a public data set on sepsis in an emergency room.We find that the hospital might optimize its process in two respects: (1) their cost-benefit balance for patient care by considering their activities in terms of continuous monitoring and substance administration, and (2) their policies on discharging patients as to ensure patients are not discharged too early and return to the emergency room.
An NLP Based Sentimental Analysis and Prediction: A Dynamic Approach

Natural language processing is an contemporary research field in which ample of work carried towards Speech processing, reorganization, intrusion, communication, emotional activity, voice over robot interaction, travel navigation, automatic car/pilot, smart appliances and many more tremendous novel community pinpointing towards machine learning and deep learning. It is an attempt to apply and process any language for an efficient Sentimental analysis and would be an alternative for applications which uses Natural Language Processing (NLP).
ORGANIZATION OF PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT OF PROFESSIONALIZATION OF COLLEGE STUDENTS

The article is devoted to the problem of professional formation of the personality of a college student during the period of study. The theoretical analysis of scientific and practical literature on the research problem allowed the authors to determine the mental reliability of the individual as a determinant of profes-sional development during the period of college education. The article describes the program of psychological and pedagogical support of students.
Modified Flower Pollination Based Task Scheduling in Cloud Environment using Virtual Machine Migration

Cloud computing allows users to use resources pay per use model by the help of internet. Users are able to do computation dynamically from different location by using internet resources. The major challenging task in cloud computing is efficient selection of resources for the tasks submitted by users. A number of heuristics and meta-heuristics algorithms are designed by different researchers. The most critical phase is the selection of appropriate resource and its management. The selection of resource include to identify list of authenticated available resources in the cloud for job submission and to choose the best resource. The best resource selection is done by the analysis of several factors like expected time to execute a task by user, access restriction to resources, and expected cost to use resources. In this paper, cloud architecture for resource selection is proposed which combines these factors and make the effective resource selection. In this paper a modified flower pollination algorithm is proposed to migrate the task on efficient virtual machine. The selection of the efficient virtual machine is calculated by the fitness function. By calculating the fitness function, the modified FPA algorithm is used to take the decision regarding VM migration is required to improve the resource efficiency or not. In this paper Virtual machine mapper maps the task as per knowledge base i.e. past history of the virtual machine, task type whether computational or communicational based. The results are compared with the existing meta-heuristic algorithms.
Strategy for Preparation of Indonesian Language Teaching Materials for Distance Learning at SMK Negeri 1 Salatiga

Teaching materials are prepared as a reference and guideline by the teacher, as well as a reflection of the teacher's evaluation which is highly effective in observing the quality of learning in the classroom.This study aims to describe how the strategy of preparing Indonesian language teaching materials in distance learning at SMK Negeri 1 Salatiga.This research uses descriptive qualitative research.The research design used is a case study.The data and sources of this research are data on strategies for preparing Indonesian language teaching materials in distance learning.The data source comes from the Indonesian language teacher at SMK Negeri 1 Salatiga.Data collection techniques in this study include observation, interviews, and documentation.The data validity technique uses source triangulation.The data analysis technique uses the Milles and Huberman interactive model.The results obtained in this study are strategies in compiling teaching materials in distance learning, including:(1) identifying learning problems, (2) analyzing student character, (3) analyzing assignments (4) formulating indicators, (5) compiling learning materials, (6) formulating learning strategies, (7) choosing appropriate learning resources, ( 8) compiling assessment instruments, (9) compiling formative and summative evaluations, (10) conducting supporting services, (11) revising learning tools
Online Learning Effectiveness during COVID-19 Pandemic a Case Study of Saudi Universities

This paper is set out to explore the students’ attitudes towards online learning effectiveness using the Blackboard platform in three public Saudi universities (Taibah, Hail, and Al-Baha) during COVID 19 pandemic. It examines the learning activities which ensure the achievement of education quality during unprecedented online learning. The survey based- questionnaire method was used to elicit students’ responses. The numbers of students who participated in the survey are 333. The main section of the questionnaire contains questions about the main online learning activities. The coefficient relation of the p-value is highly correlated when tested using Pearson’s r and Spearman’s. The score of Cronbach’s Alpha is (0.93) which indicates (greater internal consistency) an acceptable level of reliability. The findings positively emphasize the effective influence of online learning on student’s academic achievements in most of learning factors except in an assessment factor where the development of new assessment methods is needed.
An efficient ensemble framework for outlier detection using bio-inspired algorithm

Outliers are unexpected observations present in data that has to be identified systematically to prevent from catastrophic effects. The detection of outliers plays a crucial role in many application domains, as they provide exciting and critical information about abnormal events. So, in this research work, the bio-inspired ensemble-based outlier detection (BIEOD) is employed to determine the outliers. This ensemble framework combines the sequential and parallel ensembles, thereby creating a hybrid model that utilises the advantages of both feature bagging and XGBoost. The chicken swarm optimisation (CSO) is used to increase the deviation between the outliers and inliers according to the chicken competition in optimising the outlier scores. Finally, the XGBoost predicts the outliers based on the optimised feature set. The obtained results on the benchmark datasets reveal that this framework offers significant advancement over the base learners and other existing ensemble methods of outlier detection.
Introducing Digisprudence Into The Equation Of Code As Law And AI

The legal world is inundated with discussions about the somewhat elusive and confounding topic of law as code. If you are watching closely, there is another akin topic, code as law. Are they one and the same? The answer is crucial and introduces a newly coined word that we might need to focus on digisprudence underlying all those law and code confabulations.
MALARIA PREDICTION MODEL USING ADVANCED ENSEMBLE MACHINE LEARNING TECHNIQUES

Malaria is a life-threatening disease that leads to death globally, its early prediction is necessary for preventing the rapid transmission. In this work, an enhanced ensemble learning approach for predicting malaria outbreaks is suggested. Using a mean-based splitting strategy, the dataset is randomly partitioned into smaller groups. The splits are then modelled using a classification and regression tree, and an accuracy-based weighted aging classifier ensemble is used to construct a homogenous ensemble from the several Classification and Regression Tree models. This approach ensures higher performance is achieved. Seven different Algorithms were tested and one ensemble method is used which combines all the seven classifiers together and finally, the accuracy, precision, and sensitivity achieved for the proposed method is 93%, 92%, and 100% respectively, which outperformed better than machine learning classifiers and ensemble method used in this research. The correlation between the variables used is established and how each factor contributes to the malaria incidence. The result indicates that malaria outbreaks can be predicted successfully using the suggested technique.
Using BITS for conference paper conversion

As is typical for many society publishers, OSA–The Optical Society, has both a journal and a conference program. Integrating both journal articles and conference papers within a single data source opens up a pathway to conduct business intelligence analysis over the entire corpus of the published research material, which can benefit both programs and advance the society’s mission. In 2017, having successfully completed a project to convert almost 100 years of its journal legacy material to JATS XML, OSA decided to convert its conference content as well, tag it in a JATS-compatible way, and to combine both content segments in a single MarkLogic database. While it has been well-accepted that JATS and BITS cover the markup needs for journal and book content, respectively, it is less clear what Tag Set would be most suitable for tagging conference proceedings. Even though we thought “we had seen it all” in converting journal content, in the course of the project we learned that handling conference metadata and journal metadata presents very different challenges. In this paper, we share our experience with using BITS for marking up individual conference papers and how our business decisions shaped how we structure the XML. We demonstrate that because BITS was explicitly designed to enable the construction of books composed of units that could be part of many collections, the BITS metadata model is well-suited for representing conference paper’s nested collections, both event- and publication-related. To ensure data quality, we have built workflows, designed XML tools (e.g., Tag Subset, Schematron), and instituted visual QC procedures that allowed us to achieve our objective. We conclude our paper with lessons learned from this project and new opportunities its successful completion has opened up.
Efficient Identity-Based Multiple Copy Integrity Auditing Scheme for Cloud-Based IoT

Cloud-based Internet of Things (IoT) allows cloud users to achieve data outsourcing storage and flexible data access, which greatly eliminates the pressure on users to store and manage data locally. However, one risk behind this convenience is that untrusted cloud server may delete data rarely accessed or conceal the corruption of stored data to maintain its reputation. In order to verify the integrity of data remotely, a large number of public auditing protocols have been proposed. Moreover, to avoid the irreparable loss caused by data corruption, some multi-copy auditing schemes are also put forward where cloud users store multiple data replicas instead of single copy on cloud server, so that original file can be recovered from some intact replicas. In this case, how to check the integrity of multiple copies meanwhile considering the computation overhead of the auditor has become an important issue. In this paper, we propose a novel identity-based multi-copy integrity auditing scheme IBMCIA using indistinguishability obfuscation technique, which transfers most verification overhead from the auditor to the cloud server. Our IBMCIA scheme is constructed practically in medical scenario where electronic health records contain sensitive information and need to be preprocessed before uploading them to the cloud server. We introduce the role of hospital information center to implement tag generation and access management for alleviating the computation burden of patients. We prove the unforgeability of proofs under the CDH assumption and give the detailed performance evaluation.
Computer Vision Based Virtual Sketch Using Detection

Abstract: In todays world during this pandemic situation Online Learning is the only source where one could learn. Online learning makes students more curious about the knowledge and so they decide their learning path . But considering the academics as they have to pass the course or exam given, they need to take time to study, and have to be disciplined about their dedication. And there are many barriers for Online learning as well. Students are lowering their grasping power the reason for this is that each and every student was used to rely on their teacher and offline classes. Virtual writing and controlling system is challenging research areas in field of image processing and pattern recognition in the recent years. It contributes extremely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy. Given the real time webcam data, this jambord like python application uses OpenCV library to track an object-of-interest (a human palm/finger in this case) and allows the user to draw bymoving the finger, which makes it both awesome and interesting to draw simple thing. Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV
Development of an electric wheelchair that can run on slopes and uneven terrain

In recent years, various electric wheelchairs have been developed and commercialized. However, there are not many wheelchairs that can be driven on mountainous roads with large undulations or in urban areas with many steps. Therefore, development of an electric wheelchair that can run even in an environment with obstacles is required in order to extend the freedom of life for passengers. In this research we developed an electric wheelchair that can run on slopes and uneven terrain. The developed object participated in the Cybathlon 2020 Global Edition and evaluated its performance.
Knowledge distillation of convolutional neural network models on inaccurately labeled data for automatic text CAPTCHA recognition on mobile devices

Problem definition: most of the existing works investigate the recognition of a fixed-length CAPTCHA, but the authors suggest using knowledge distillation to simulate the operation of recurrent-convolutional models, which have proven themselves well in the task of predicting the dynamic length of characters in images. The rapid development of deep learning systems, the recognition quality of which has reached the level of human vision, makes the method of protection using CAPTCHA increasingly ineffective. In addition, such protection imposes high requirements on the characteristics of the devices on which recognition is performed. The research carried out in this work allowed us to propose an effective method of training CNN on inaccurate data for automatic circumvention of text CAPTCHAS on mobile devices. Purpose: acquiring a lightweight and high-quality model for text CAPTCHA recognition that can work on mobile devices. Results: the paper describes a method for training a lightweight model on inaccurate markup obtained from another model. The influence of the size of the training sample on the quality of recognition, the speed of the model on various end devices is studied on the example of a popular social network. Practical significance: The proposed method allows you to train convolutional models to bypass the protection of websites-text CAPTCHA, which are undemanding to the characteristics of devices. The analysis of the model errors allows us to make recommendations for improving ways to counteract automatic recognition.