Reviewerly/exHarmony huggingface dataset:
Technical Capability, Instructional Strategies and Learners’ Engagement in Online Learning Instruction: Basis for Framework Development

Schools are faced with the challenge of developing and implementing an online framework model especially in Radiology technology program.Online courses offer an excellent way for students to broaden their educational opportunities and stay competitive in the everdemanding realm of education.Learning engagement in technology mediated learning and its effect on learning effectiveness and satisfaction, and their experimental data showed that the effects of technology-mediated learning were mediated by learning engagement, learning engagement had a positive effect on learning effectiveness whether in face-to-face learning or technology-mediated learning.Moreover, effective design of instructional materials elicits appropriate cognitive processes in the learner and mediates more successful learning outcomes ensuring an engaging and worthwhile online learning is necessary to achieve the same result of achieving quality learning, therefore it is important to address these topics on faculty technical capability, standard instructional strategies and learner's engagement strategies to the development of dynamic and an effective online framework model for Radiology technology program.This study aimed to assess the teachers' level of technical capability, instructional strategies utilization and level of learners' engagement strategies that set as the basis to enhance the online framework model for radiology technology program.Descriptive correlational research design was employed.The study revealed that Radiologic Technology Professors in terms of technical capabilities, instructional strategies and learner's engagement had a very confident concept in online learning.In essence, the results yield the delivery of education with the use of technology introduces a platform that gives a more accessible learning system.
Causal topology and non-material causes

The principle of physical causal closure makes the problem of the ontology of consciousness insoluble since the principle rejects the possibility of mental causation. However, this principle is based on the linear causality model the foundation of which is the connection of material events with each other. The article deals with the possibility of mental causation based on non-material events, reflecting the influence of probability on probability, and not an event on an event. A hypothetical model is considered, in which one state of activity of neural processes in the brain can correspond to various mental states that differ from each other, however, not content-wise but within the framework of a non-material property. The non-material property is defined as the likelihood of mental assessments and, in essence, is the causal topology of mental processes.
Classification of Colorectal Cancer Histology Images Using Image Reconstruction and Modified DenseNet

Colorectal cancer is one major cause of cancer-related death around the globe. Recent breakthroughs in deep learning have paved the way to apply it for the automation of histopathology images as a tool for computer-aided diagnosis of medical imaging. Here we have presented a novel state of the art classification model for classifying the colorectal histopathology images into 9 classes. All the traditional approaches like texture-based classification, transfer learning etc. already has been used to achieve a state-of-the-art result, but these have some limitations. Rather than using conventional mechanisms, we have proposed a methodology that can interpret the histopathology images in a more generalized way without image preprocessing and augmentation mechanisms. A combination of two deep learning architectures i.e., an encoder unit of autoencoder module and a modified DenseNet121 architecture are used for this purpose. An accuracy of 97.2% on Zenodo 100k colorectal histopathology dataset has been reported. The presented result is better than most of the contemporary works in this domain. We have also evaluated the effectiveness of the current approach for the low-resolution histopathological images and achieved good recognition accuracy.
ORGANIZATION OF GROUP ACTIVITIES OF OLDER PRESCHOOL CHILDREN IN THE PROCESS OF EXPERIMENTING

The authors of the article analyze the advantages and disadvantages of group activities of older preschool children. The article focuses on the importance of children's experimentation for the versatile development and readiness of older preschool children to study at school. A comparative characteristic of the organization of individual children's experimentation and a group of older preschool children is presented. Based on a survey of older preschool children and their teachers, as well as monitoring the activities of children during experimentation, problematic aspects of organizing group activities of children are identified. Psychological and pedagogical conditions for organizing group activities of older preschool children for effective interaction during experimentation are presented. The expediency of using experimentation as a means of developing children's research activity is substantiated. The specifics of experimentation are indicated. The purpose and structure of experimentation as a kind of games for children's cognitive development are determined. The article also contains a list of devices and materials for arranging the experimentation centre which should be located in a preschool institution according to the thematic principle and teacher’s work plan; due to such centre children will be able to further freely experiment and consolidate the learned material through independent search activities. It is proved that the use of psychological and pedagogical conditions for the organization of group activities of older preschool children in the process of experimentation will contribute to the formation of their beliefs about nature and the world.
Mondrian Based Real Time Anonymization Model

The presence of private information belonging to individuals in data heaps called "Big Data" causes the privacy of the person to be endangered against disclosure attacks. To protect personal privacy in big data, it is ensured that anonymous data is created, stored, and shared in systems with anonymization methods. However, de-identified data cannot be reinstatement. The aim of this study is to create a new method that provides instant disidentification and does not disrupt the data structure in the system. In the study, the Hadoop ecosystem was used to process large data heaps. With the proposed model, it has been ensured that the requests from the user are processed in the Hadoop ecosystem with the services in the middle layer, thus obtaining anonymous data. The algorithm used for disidentification is optimized and results are compared according to algorithms in the literature. With the proposed model, it has been observed that the user is user-friendly in terms of querying and obtaining an anonymous data set. According to the analysis results, an algorithm that works with 40% efficiency compared to other algorithms in terms of processing speed was created with the disidentification algorithm used in the model.
Zebrafish Histotomography Noise Removal In Projection And Reconstruction Domains

X-ray “Histotomography” built on the basic principles of CT can be used to create 3D images of zebrafish at resolutions one thousand times greater than CT, enabling the visualization of cell nuclei and other subcellular structures in 3D. Noise in the scans caused either through natural Xray phenomena or other distortions can lead to low accuracy in tasks related to detection and segmentation of anatomically significant objects. We evaluate the use of supervised Encoder-Decoder models for noise removal in projection and reconstruction domain images in absence of clean training targets. We propose the use of a Noise-2-Noise architecture with U-Net backbone along with structural similarity index loss as an addendum to help maintain and sharpen pathologically relevant details. We empirically show that our technique outperforms existing methods, with an average peak signal to noise ratio (PSNR) gain of 14. 50dB and 15. 05dB for noise removal in the reconstruction domain when trained without and with clean targets respectively. Using the same network architecture, we obtain a gain in structural similarity index (SSIM) in the projection domain by an average of 0.213 when trained without clean targets and 0.259 with clean targets. Additionally, by comparing reconstructions from denoised projections with those from original projections, we establish that noise removal in the projection domain is beneficial to improve the quality of reconstructed scans.
Formation of Professional Competence of University Students Based on a Systematic Approach

The purpose of this article is to demonstrate the feasibility and effectiveness of a systematic approach to the formation of professional competence of students in higher educational institutions. The study was conducted in the S. Seifullin Ka-zakh Agro Technical University with the participation of 126 master students (2 groups) of the specialty “Vocational Education”. Using a complex of theoretical, empirical and statistical methods, it was possible to show the ways to increase the effectiveness of the educational process. The basic principles for the training of students were formulated as: 1) systematic approach to the formation of profes-sional competence, organization and implementation of the educational process; 2) program-targeted formation of the indicative basis of the upcoming profes-sional activity, and 3) creating a competency model of the corresponding special-ist as a hierarchy of the student’s professional competency system, its subsys-tems, and supersystems. It is recommended that Kazakhstan universities should improve tools and procedures for assessing and monitoring students’ level of knowledge, as well as create new programs for developing students’ professional competencies to ensure their successful employment. The further research on the matter may be the systematic comparative study of contemporary teaching meth-ods and techniques to determine the most productive ways to prepare future pro-fessionals.
Touch-Point Detection Using Thermal Video With Applications to Prevent Indirect Virus Spread

Viral and bacterial pathogens can be transmitted through direct contact with contaminated surfaces. Efficient decontamination of contaminated surfaces could lead to decreased disease transmission, if optimized methods for detecting contaminated surfaces can be developed. Here we describe such a method whereby thermal tracking technology is utilized to detect thermal signatures incurred by surfaces through direct contact. This is applicable in public places to assist with targeted sanitation and cleaning efforts to potentially reduce chance of disease transmission. In this study, we refer to the touched region of the surface as a "touch-point" and examine how the touch-point regions can be automatically localized with a computer vision pipeline of a thermal image sequence. The pipeline mainly comprises two components: a single-frame and a multi-frame analysis. The single-frame analysis consists of a Background subtraction method for image pre-processing and a U-net deep learning model for segmenting the touch-point regions. The multi-frame analysis performs a summation of the outputs from the single-frame analysis and creates a cumulative map of touch-points. Results show that the touch-point detection pipeline can achieve 75.0% precision and 81.5% F1-score for the testing experiments of predicting the touch-point regions. This preliminary study shows potential applications of preventing indirect pathogen spread in public spaces and improving the efficiency of cleaning sanitation.
BLOCKCHAIN TECHNOLOGY IN HEALTHCARE APPLICATIONS

Blockchain technology has been gaining great importance in recent years owing to its private, reliable, secure and trustworthy information exchange among peer entities. This technology has been increasingly used in numerous applications such as financial services, remote sensing, food science, internet of things, brain research and healthcare industry. Data immutability, transparency and decentralization nature of blockchain attracts the researchers to make use of this technology in various fields. Healthcare records contain confidential patient data that needs to be stored in secure manner. Blockchain technology considerably plays a vital role in maintaining the confidentiality of medical data. Also, blockchain provides effective and secure data sharing which results in effective diagnosis and treatment. This paper highlights the development of blockchain applications in healthcare system and its challenges.
Overview of Intelligent Tutoring System in Human Teamwork and Its Impact Generally on Learning

Teams can be depicted in many organizations and is becoming increasingly used for effective and efficient productivity.Teams work as the collection of individuals that have different skills to complement each other rather than compete, their driving force is to achieve a common goal by inculcating strategical approaches of which they are held accountable.This, helps in maximizing general output, however the need of its incorporation in learning.This study presents an overview of Intelligent Tutoring System and its impact generally on learning.A review of previous works done on this area was also inculcated in this study highlighting their importance and relevance.Intelligent Tutoring System (ITS), been a machine intelligence learning paradigm.This research explores and unveils what an intelligent tutoring system is all about and its role in learning.The work also covers how intelligent tutoring system has enhanced learner's skill and performance.In conclusion, the massive effects of intelligent tutoring system on the learning process are acknowledged.
A domain categorisation of vocabularies based on a deep learning classifier

The publication of large amounts of open data is an increasing trend. This is a consequence of initiatives like Linked Open Data (LOD) that aims at publishing and linking data sets published in the World Wide Web. Linked Data publishers should follow a set of principles for their task. This information is described in a 2011 document that includes the consideration of reusing vocabularies as key. The Linked Open Vocabularies (LOV) project attempts to collect the vocabularies and ontologies commonly used in LOD. These ontologies have been classified by domain following the criteria of LOV members, thus having the disadvantage of introducing personal biases. This article presents an automatic classifier of ontologies based on the main categories appearing in Wikipedia. For that purpose, word-embedding models are used in combination with deep learning techniques. Results show that with a hybrid model of regular Deep Neural Networks (DNNs), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), classification could be made with an accuracy of 93.57%. A further evaluation of the domain matchings between LOV and the classifier brings possible matchings in 79.8% of the cases.
Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.
Third Order Mathematical Model of the Brain Validated via Cerebral Evoked Potentials

This paper presents the vital role mathematical modeling plays in the analysis of evoked potentials obtained from auditory, somatosensory, visual, and cognitive stimuli.Analysis of this data was performed by using the concepts of Closed Loop Methodology (CLM) to create specific mathematical modes representative of the signals that were being analyzed. As a result of this effort, a Forensic Simulator was design and built to signal process the EEG's provided by patients of the Craig Hospital Neuroscience Laboratory located in Englewood, Colorado.The Forensic Simulator provides a modern system analysis tool for neuroscience research. This device provides a user friendly environmental from which the electrical activity of the brain can be quantified. By using an adaptive processor to null out the error generate by comparing measured evoked potentials to those produce by a mathematical model, the neurophysiology of the central nervous system may be evaluated. Nulling of this error is achieved by defining the neurological classification set. Members of the set are defined to be: the uncertainty of the neuron firing, the neuronal staee characteristics, the characteristic brain parameters, estimates of the neuronal states, variances associated with the neuronal state estimates, and the variances attributable toe measurement error.Members of the classification set are obtained by solving the modeling problem via solution to the subproblems of estimation, identification, and control. Algorithms solving the the estimation problem provide numerical time varying values for the states of the system. This ability is assured once the SID (System IDentification) has been solved. This solution yields the quantification of the uncertainties associated with the state estimates as well as the uncertainties associated with the evoked potential measurements.
Secure Cloud Medical Data Using Optimized Homomorphic Encryption

Protection of medical images on the virtual transmission platform is a simple and demanding activity, using multiple techniques to protect the digital file, such as cryptography, steganography and watermarking. This are the digital image protection approaches to meet security goals, i.e. confidentiality, trustworthiness, and usability. In this paper, the medical images are deposited in the cloud utilizing optimized homomorphic encryption. To increase the security level Rider Optimization Algorithm (ROA) is utilized to select the optimal key. Homomorphic encryption is a method of authentication which helps one to perform observes of the encrypted information through decrypting it. When the data is decrypted, the calculation consequence is stable, the same as if the procedures were conducted on the unprotected data. After completing the encrypted data, the encrypted images can be stored in the cloud and the decryption the same process is utilized to reconstruct the original images. For the experimental analysis, the performance of the proposed method is analyzed by the various parameters such as execution time, encryption time, and decryption time. The execution time of the proposed method is 2% decrease the HE existing method and 4% reduce the ECC algorithm.
Multi-Dimensional Spatial Attention Residual U-Net (Msaru-Net) for Low-Dose Lung Ct Image Restoration

Low-dose computed tomography (LDCT) is considered as the most effective method for early lung cancer screening in the clinic. However, it's still a significant challenge to ensure the quality of reconstructed images under the premise of reducing the radiation dose. To reduce the interference of image quality degradation on the diagnosis of radiologists, in this paper, we introduce a multi-dimensional spatial attention residual U-net (MSARU-net) model to improve the low-dose lung CT images quality. The proposed MSARU-net combines residual U-net, multi-dimensional feature extraction module and spatial attention mechanism to retain critical structural information of different levels while suppressing image noise. Experiments with simulation LDCT data have shown its superiority in visual effects and quantitative measurements, indicating the potential of achieving a noticeable gain compared to the state-of-the-art methods.
Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)

This paper details a semi-automatic method of word clustering for the Algonquian language, Nêhiyawêwin (Plains Cree).Although this method worked well, particularly for nouns, it required some amount of manual postprocessing.The main benefit of this approach over implementing an existing classification ontology is that this method approaches the language from an endogenous point of view, while performing classification quicker than in a fully manual context. 1 There is one attempt at semantically classifying Nêhiyawêwin through automatic means found in Dacanay et al. (2021).This work makes use of similar techniques as desccribed in this paper, differing mainly in its mapping of Nêhiyawêwin words onto Wordnet classes.contained two or more clear semantic groupings.Through manual postprocessing, the following set of classes were defined: VTA_allow, VTA_alter, VTA_body-position, VTA_care-for, VTA_cause, VTA_clothes, VTA_cognition, VTA_create, VTA_deceive, VTA_do, VTA_existential, VTA_food, VTA_hunt, VTA_miss/err, VTA_money, VTA_move, VTA_play, VTA_restrain, VTA_religious, VTA_seek, VTA_sense, VTA_speech, VTA_teach, VTA_tire, VTA_treat-a-way, VTA_(un)cover
Self-guided quantum state learning for mixed states

We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation algorithm and applies to mixed qudit states. The salient features of our algorithm are efficient post-processing in dimension d of the state, robustness against measurement and channel noise, and improved infidelity performance as compared to the contemporary adaptive state learning algorithms. A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.
Face Recognition Using Face Embedding Method

The recognition of faces is of great significance for real worlds applications like video surveillance, classroom students attendance recording, human-machine interaction, security systems etc. It is still several challenges to detection and recognition for recognising multiple faces because it is not easy for detecting multiple faces in one frame, it is also hard to recognise faces with low resolution. Compared to machine learning traditional approaches, approaches based-on deep learning shown better performance in terms of processing speed and accuracy in face recognition. Our method uses a combination of Viola Jones, Face-net and Support Vector Machine (SVM), and achieved an accuracy of 94% for 100 person through 166s and for real time face recognition, it achieved an accuracy of 100% for 10 frames through 9 seconds.
ALEXANDROFF SPACES AND GRAPHIC TOPOLOGY

This work studies and gives some conditions for an Alexandroff space to be graphic topological space by using some basic properties of graphic topology such as locally finitely property. That is, we offer some answer for the open problem which is recalled in \cite{AJK} (Problem 2 page 658).