Reviewerly/exHarmony huggingface dataset:
Innovative Socio-Cultural Project Development Realized by The Leaders of Folklore Groups to Preserve and Develop the National Folk Culture

The article is devoted to innovative socio-cultural project development as a way of preserving and developing folk culture. In the modern world, far from traditional culture, folk rituals and art must penetrate life itself, as it was two hundred years ago, and accompany daily events and activities. Innovative socio-cultural project development contributes to this. The conceptual justification for the implementation of the laws and principles of innovation was carried out by M.M. Potashnik, L.S. Podymova and A.V. Khutorskoy. The essence of innovation was revealed by F. Bezrudnyi, V.S. Dudchenko and O.S. Sovetova. A special area of cultural innovation is the ethnocultural sphere; its features were considered in the works by N.G. Mikhailova, L.L. Khristiansen and A.S. Iareshko. The study reveals the features of project development in the field of culture and leisure, which consist in the fact that it is aimed at updating existing or developing and introducing new technologies significant for culture and priority artistic and spiritual values. The authors have systematized modern projects in the field of folk culture related to the creation of audiovisual products, organization of festivals and celebrations based on the materials of regional song folklore, artistic and creative recreation of folk clothes, jewelry, crafts and ceremonies, stage productions, as well as new forms of cultural activity, including quests, cities of folk crafts, virtual travel. The study may be useful to teachers and methodologists in developing educational programs in universities of culture and art, to social and cultural workers in organizing and conducting socio-cultural projects related to folk culture, to heads of ethnocultural centers and amateur and professional groups in conducting artistic and creative activities, as well as scientific researchers of modern sociocultural processes. The novelty of the study is associated with its conclusions. Based on the fact that the socio-cultural sphere has acquired both a non-commercial and commercial nature today, a head of a folklore collective must be an economically and legally competent specialist, whose activity correlates with the laws of cultural- and socio-historical, as well as modern, processes and experience of social communication and interaction. For successful development of innovative thinking in leaders of folk groups and institutions in the cultural sphere, scientifically based information is needed, concerning both structural and procedural characteristics of innovative thinking and socio-psychological determinants of its development and implementation.
Design of a Mobile App to Digitalize Teachers’ Professional Journals in the Practicum

The Teaching practicum is probably the most genuine period of professional training at the teaching degree. The use of tools such as the teacher’s professional journal helps to reflect and systematize the experience of the teacher in training. This chapter includes the design of a mobile application to facilitate the use of the teacher’s professional journal through its digitalization in order to experience the benefits it brings as a tool that favors the teacher’s own practice and professional development. An analysis of the content of 20 teacher journals written by students of Infant Education at the University of Salamanca was made with the aim of extracting the main reflection topics. The result was a carefully produced indexing tree that has been used as an analysis tool in the NVIVO12 software for the study of qualitative data. The tree allowed extracting a significant and complete categorization of the teacher’s professional journal that makes possible its analysis and constitutes the starting point for the design of the prototype of the mobile application in a paper, which will be developed. The digitization of the professional teachers’ diary based on the most frequent topics of the teachers themselves in practice shows the relevance of the diary since it is based on a thematic index that arises from practice, not theory. This will favor its implementation both by teachers in training and by active professionals contributing to their professional development.
Feed forwarded CT image registration for tumour and cyst detection using rigid transformation with HSV colour segmentation

This paper automates the medical diagnosis process by image registration feed forwarded to object detection with prediction of tumour and cyst by using K-means clustering over HSV colour features. Diagnosis of life killer disease is a complex process which requires bio-medical image such as MRI, CT, and endoscopy etc. Many biomedical images are used for the same case to predict the disease. Because of different view point of different photographic sensors at different time obtained medical images are not aligned. So the manual diagnosis makes harder because of the images are not registered or not aligned properly. The inherent cause is the distortion of the imaging signal where object may be miss-transformed due to different camera focus and projection. Image registration is an essence to bypass the non-alignment issue. Here, we have proposed and analysed a combined solution towards the miss aligned object or region of interest by performing reverse geometric transformation with different angle to produce the better perspective image for diagnosis which is feed forwarded to HSV colour model-based segmentation to predict the cyst and tumour presence.
A Secured Supply Chain Management System for Pharmaceutical Companies using Blockchain

Digitalization is spreading all over the world in a vast way.This work presents us with a different prospective and view upon the use of the most recent technology of the market that is blockchain.An effective supply chain management is the biggest challenge in every industry.In healthcare, there is additional risk and complexity as a compromised supply chain in healthcare can affect the patient's safety.Increased adoption of technology, globalization and industry populated with multiple stakeholders in various jurisdictions has given rise to a complicated health supply chain.The pharmaceutical supply chain is one of the most prominent verticals when it comes to the damaged areas of health supply chain.A lot of patients endure due the utilization of terminated and falsified meds and gadgets.Patients are unconscious of systems to see whether the medication is consumable or not.For solid and legitimate data to shoppers from the pharmaceutical organizations, Blockchain gives adaptable however made sure about information passage and extraction.Information Stored is homogeneous and predictable all through the framework at any of the companions.Implementation of Blockchain in pharmaceutical supply chain, not only reduce counterfeiting and theft issues but also help manage inventory.
Survival Outcome Prediction for Breast Cancer Patients

The second most causative disease is breast cancer happening in women and a significant explanation behind expanding death rate among women. Observed rates of this cancer are increasing with industrialization and also with early detection facilities. As the finding of this ailment physically takes extended periods and the lesser accessibility of frameworks, there is a need to build up the programmed determination framework for early identification of malignant growth. We have used machine learning classification techniques to categorize benign and malignant tumors, in which the machine learns from past data and predicts the new input category. Models like logistic regression and Random Forest are Done on the UCI dataset. Our experiments have indicated that Random Forest has the best prescient examination with exactness of ~96%.
Arabic literal amount sub-word recognition using multiple features and classifiers

Bank check processing is an important application of document analysis and recognition. Recognising the literal amounts from the check images is challenging and an open research problem. In this paper, we present our work on Arabic bank check literal amounts' sub-word recognition using four sets of features and three classifiers namely: support vector machine (SVM), neural network (NN), and decision tree forest (DTF) classifiers. In addition, we investigated two different approaches for classifier fusion. We tested our system on the CENPARMI database of Arabic bank check images. Our recognition results outperform previous published results on the same database.
Visual document tracking and blockchain technologies in mobile world

As during the last decade the limit between professional and personal usage of smartphones gradually disappeared, the present study is devoted to the tracking of the visual documents scanned by a personal mobile phone for some professional reasons. By a visual document we assume a composition of text, graphics and images corresponding to various physical-world documents (invoices, calls for tenders, legal contracts, etc.). As the scanning (capturing) conditions cannot be reproductible, the main issue is to unambiguously and securely identify various digital representations for a same physical document. A second issue is related to the inherent constraints in resources made available for such a task in the mobile/embedded environment. To jointly solve these issues, a solution based on coupling the blockchain technologies to the visual fingerprinting principles is advanced. The novel elements thus brought to light relate to (1) the coupling of fingerprint and blockchain solutions, (2) the unitary smart contracts generation and management (with illustrations for the Tezos blockchain) and (3) an on-chain / off-chain work balancing solution for coping to the mobile world constraints. The experimental results obtained on a database of more than 10 000 visual documents resulted in F1 score equal up to 0.98 while being compatible with low-resources computing environments (Raspberry Pi).
Verifiable and Privacy-Preserving Ranked Multi-Keyword Search over Outsourced Data in Clouds

With the prevalence of cloud computing, more and more individuals and companies are entrusting data storage and heavy computing tasks to the cloud server. Cloud computing provides services for obtaining encrypted documents on demand, so that users can selectively retrieve encrypted documents according to some keywords. Although outsourced computing and storage brings a lot of convenience, it also raises privacy concerns. In this paper, we present a verifiable and privacy-preserving ranked multi-keyword search (VPS) scheme based on the difficulty of factorization on large integers. Data users can preset a threshold in advance, and the cloud server can return search results with the similarities greater than the preset threshold. Also, our proposal allows data users to verify whether the search results returned by the cloud server are correct. Extensive experimental evaluation and comparison indicate that our scheme is efficient and practical.
Building a conducive, engaged, and learning working environment through sustainable and impactful organisational culture

A positive working environment that is conducive to engagement and learning among an organisation's employees plays an integral role in the overall success and sustainability of the organisation itself. This study provides an in-depth analysis of using organisational culture as a sustainable tool to bring about lasting changes in the organisational ecosystems that contribute to employee engagement and lifelong learning. Samples from 472 respondents were obtained, and SEM analyses were employed in this study. The empirical results demonstrate that bureaucratic organisational culture has no impact on engagement and lifelong learning among employees. Although innovative organisational culture is the most effective in promoting employee engagement, it does not impact employee lifelong learning disposition. Interestingly, supportive organisational culture negatively impacts engagement, but contributes to workforce lifelong learning (LLL). This research shows that a conducive working environment can be created with proactive nurturing of organisational knowledge as the inherent culture of the organisation.
Verb Based Sentiment Research

Sentiment Analysis is one of the leading research work. This paper proposes a model for the description of verbs that provide a structure for developing sentiment analysis. The verbs are very significant language elements and they receive the attention of linguistic researchers. The text is processed for parts-of-speech tagging (POS tagging). With the help of POS tagger, the verbs from each sentence are extracted to show the difference in sentiment analysis values. The work includes performing parts-of-speech tagging to obtain verb words and implement TextBlob and VADER to find the semantic orientation to mine the opinion from the movie review. We achieved interesting results, which were assessed effectively for accuracy by considering with and without verb form words. The findings show that concerning verb words accuracy increases along with emotion words. This introduces a new strategy to classify online reviews using components of algorithms for parts-of-speech..
Decision Trees

Decision tree is probably the most intuitive data classification and prediction method. It is also used frequently. While most of the data mining methods we have learned are parametric, decision tree is a rule-based method. The most critical concept in understanding decision trees is entropy which will be explained soon. A tree is composed of nodes and the leaves are the bottom nodes. At each node except for the leaf nodes, a decision must be made to split the node into at least two branches. Figure 9-1 depicts a sample decision tree structure.
Reliability and accuracy of systems of the natural environment control. Part 3

The assessment of the feasibility of hydrophysical experiments in typical frequency ranges of variability of random processes with power-law spectra (-5/3), taking into account the reliability and accuracy of measuring instruments and methods of conducting experiments is carried out. It is shown that, at the current level of technology, a non-recoverable system can implement experiments with a probability of 0.95 only for small-scale and mesoscale processes. The requirements for the minimum reliability of the nodes of the probabilistic space-time lattice of measurements for the given parameters of experiments are determined. It is shown that a non-recoverable system with a reliability of 0.99 is realizable only for small-scale and mesoscale processes. It is shown that the experimental error depends on reliability extremely for rough and unreliable systems and monotonically for accurate and reliable systems.
AN INTELLIGENT AUTOMATIC ATTENDANCE MANAGEMENT SYSTEM FOR STUDENTS USING FACE RECOGNITION

One of the fastest and foremost emerging and interesting technology is FaceRecognition.In this fast-evolving generation, attendance is usually a problem among the school community and so as the business community.one among the foremost efficient solution for this problem is that the Digital Attendance System.Nowadays Computers play a vital role in many fields.Likewise, in this field also, we will make an efficient use of it.This unique scholar's attendance system based on face recognition, is a method to identify and recognizing faces of the student with the help of images captured from a high-quality camera and uploading the attendance of every student automatically, rather than manual attendance uploading process.In our face recognition project, a smart system is going to be ready to capture a frame from a video and process it accurately and predict which of the scholar is present therein class.There are some algorithms and programs to detect the face of an individual, but the concept to be implemented here is Neural Networks.In this we have used a neural network, we will capture the image of class and extract the face image, then pre-process it for a clear face, and will be given to the trained network so the face of the student will be often identified and updated automatically within the database.I.
The Application of Mobile-Enhanced Collaborative Learning Models on Oral Presentation Competence in Rural Area During Covid-19 Pandemic

Oral presentation skills are regarded as one of the most critical abilities required to achieve success in higher education and future careers. Therefore, this study focuses on examining the use of mobile-enhanced collaboration learning models to assist rural students in studying oral presentation skills. This involved 80 students in an online academic presentation class conducted through the implementation of video blogging. Data were collected from oral presentation videos and interviews while a modified Malouff and Shearer grading rubric was used to assess the students' presentation video and interview guide. The findings showed that the mobile-enhanced collaboration assists students, particularly those living in rural areas, to improve their oral presentation skills even during the period of the pandemic that they were forced to study from home. It is important to note that the students used the applications they deemed appropriate and accessible to accomplish their goal of creating a proper script and a professional video presentation. This means it is possible to provide students with a rich learning opportunity using oral presentation teaching models in order to enhance their future presentation skills.
Power GCD and power LCM matrices defined on GCD-closed sets over unique factorization domains

Let T = {t 1 , t 2 , . . ., t m } be a well ordered set of m distinct positive integers with t 1 < t 2 < • • • < t m .The GCD matrix on T is defined as (T ) m×m = (t i , t j ), where (t i , t j ) is the greatest common divisor of t i and t j , and the power GCD matrix on T is (T r ) m×m = (t i , t j ) r , where r is any real number.The LCM matrix on T is defined asis the least common multiple of t i and t j , and the power LCM matrix on T is [T r ] m×m = [t i , t j ] r .Set T = {t 1 , t 2 , . . ., t m } is said to be gcd-closed if (t i , t j ) ∈ T for every t i and t j in T .In this paper, we give a generalization for the power GCD and LCM matrices defined on gcdclosed sets over unique factorization domains (UFDs).Moreover, we present a speculation for a generalization of Bourque-Ligh conjecture to UFDs which states that the least common multiple matrix defined on a gcd-closed P -ordered set in any UFD is nonsingular.Some examples that show what is done are additionally given in Z [i] and Z p [x].
Identity authentication-based load balancing with Merkle hash tree for secured cloud data storage

Cloud computing allows users to store and process the data in the cloud in a simple manner. However, cloud security and load balancing remain as demanding issues in cloud server. An identity authentication load balancing based Merkle hashing (IALB-MH) technique is introduced for the user to access the cloud services with unique identity (ID). Initially each cloud user sends their ID to the CS for registration. The cloud service provider checks whether the user has authenticated or not by matching the cloud user ID with the ID in CS. Therefore, a load balancing is performed by avoiding the unauthorised access to data. A Merkle hash tree is constructed to store the user data with hash values in a secured manner with minimal storage space. Experimental evaluation is carried out on load balancing efficiency, attack detection rate, space and time complexity with respect to a number of cloud users and data.
Integrity Checking in Cloud Storage and Policy Based User Revocation using Attribute Based Encryption

Applications that range over various mists are frequently observed to be powerless against security dangers. Such profoundly heterogeneous situations need a fine-grained access control system like Attribute-based Access Control for upholding security. One of the most encouraging applications in distributed computing is online information sharing. Step by step instructions to share client information securely and productively has turned out to be one of the most testing issues in distributed computing. Be that as it may, this postures new provokes identified with making secure and solid information stockpiling over questionable specialist co-ops. The principle objective of distributed computing is the means by which to verify, secure the information and procedure. In this study, we find the issue of guaranteeing the respectability of data in cloud computing. Specifically, we consider techniques for decreasing the weight of producing a consistent measure of metadata at the customer side. Ascribe division chooses whether to momentarily disavow client approval as indicated by property subset. Furthermore, we propose another model called Policy-based Attribute Based Access Control (Pa-ABAC) utilizing half breed property based encryption. Our goal is to officially determine the conduct of various components of the proposed model in a cloud domain. This is imperative to build up a substantial security arrangement for a cloud situation free from determination mistakes and irregularities.
Mood Detection using Facial Recognition Technology Application in Higher Education Institution

Mood detection is an important trait that human can do using face expressions and it could be learned by the machine using machine learning approaches and technology.This research utilized a deep learning algorithm to develop a model that can detect various mood of participants in order to identify their subject areas of interest when enrolling in higher education institution so the academic advisor can smartly advise and guide the students in selecting the right program when they are seeking admission to the college.The methodology used in this research is application of the pre trained model technology deployed by convolution neural network for analyzing the facial expression on participants through three steps namely: image processing, facial recognition and mood detection.The mood detection by facial recognition technology is used to test independent input images of 15 participants to detect whether the mood is "happy", "sad", "neutral" and "surprise".The student's mood is determined by the facial movement this can further help in guiding the students for the proper program selection based on their facial expressions.A quasi experimental approach has been taken to analyze the results.The research findings show that the developed model was able to predict and classify various mood quiet accurately such as "happiness" and "neutral".On the other hand, difficultly detect other moods such as "surprise" and "sad".The study shows promising results and great capability of detecting human moods that has lots of applications such as academic advising in academic institution.The research is yet a good contribution in identifying a student's facial expression that positively effects the academic advising process and it is expected to better understand the student's needs and emotions.
Multiple Authority Based Data Fragmentation Technique for Providing Secure Storage in Cloud

The existing works on security storage in cloud computing cause overhead, data correctness issue, key escrow problem, single point of failure and so on. This develops Multiple Authority based Data Fragmentation Technique for providing Secure Storage in Cloud Computing. It aims to avoid the key escrow and single point of failure issues of secure storage. In this technique, the Cloud Service Provider (CSP) provides multiple services for a client, the trusted and verification authorities are responsible for jointly creating the master key and system parameters and the Cloud Data Owner (CDO) encrypts the documents using CP-ABE. In order to provide data recoverability, a file is divided into blocks and sectors. Probabilistic algorithm checks the integrity of fragmented file. By simulating, the proposed technique reduces the overhead and data failures.