Reviewerly/exHarmony huggingface dataset:
Representation of the concept of the artistic image of Kobzar

The article deals with the possibilities of representation of an artistic image with the help of computer technologies, which is implemented in the scenario of a musical and theatrical miniature dedicated to the representation of the concept of T.&nbsp;Shevchenko’s work. The artistic image of Kobzar created by multimedia is interpreted as a character in action, which directly complements other – real – participants. The literary and musical canvas of the action was the poet’s song samples selected in order to reproduce the typical features of the Ukrainian worldview with dominant accents of the lyrics. In addition to the classical means of theatrical drama, the organization of the artistic material in action was significantly influenced by the peculiarities of the drama of the cinema and the drama of musical genres with constructive elements of musical forms. In particular, the determination of the dynamic cross-cutting final section by the smooth “flowing” of simple “song forms” caused crescendo type deployment of thematic material. One of the dramaturgical “accent” was the creation of conditional-simultaneous action, hidden (“horizontal”) scenic two-dimensionality in “small” cyclical “contexts” of relatively closed musical formations and the final culminating “descent into the epicenter” of all previous figurative and thematic lines according to the principle “lyrical cinema” with a dramatic ending. In the created artistic continuum, the “temporal equivalents” of the past-present-future are “baroque intertwined” in a single artistic plane based on a single complementary whole emerges. Moreover, the mental picture of the spiritual existence of Ukrainians is generally represented. The experience of the integrated application of computer technologies in the development of an idea, scenario and their implementation in an artistic event based on the Lviv Krushelnytska State Music Lyceum can be successfully extrapolated to art projects of humanitarian disciplines of general secondary and specialized art education institutions. We believe that despite the philosophical undertones of the action, enhanced by its staging, its implementation is within the reach of both primary and&nbsp; secondary school students. &nbsp; References: Skrypnyk, H. (Ed.). (2008). Ukrainska muzychna entsyklopediia: Vol. 2 [Ukrainian musical encyclopedia]. Kyiv: Vydavnytstvo Instytutu mystetstvoznavstva, folklorystyky ta etnolohii NAN Ukrainy. Shevchenko, T. (2013). Kobzar [Kobzar]. Kyiv: Veselka. Kadeshchuk, I., &amp; Plishkyi, A. (Eds). (2014). Shevchenko u pisniakh [Shevchenko in songs]. Suchava: Lidana.
Mining Deeper Insights from Texts Using Unsupervised NLP

An industry or a business collects huge amount of free form text through internal processes such as typed call center notes, notes typed by document processing teams, etc. or from users through customer feedback mechanism and process them. Many of these free form texts come without labels on what the texts are about, and no supervised learning models could be used to extract insights from these texts. For instance, guest feedback after a hotel stay or feedback after a Restaurant visit might have valuable information much more than just customer sentiments, such as specifics on what was good and what was bad. Manually labeling this information on the texts is erroneous and involves time and effort. Unsupervised learning from machine learning combined with the power of vector algebra can be used to mine deeper insights from these texts. These insights could act as labels for building new supervised models if needed and these predicted insights could be converted to actionable business outcomes through visualizations.
CONDITIONS OF PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT OF THE PROCESS OF FORMATION OF THE IMAGE CULTURE OF TEACHERS

The problem of forming image culture of teachers in the conditions of the developing contemporary education is actualized. The essence and functions of image culture are considered on the basis of the analysis of psychological and pedagogical publications. The necessity of psychological and pedagogical support of teachers’ image culture formation is proved. The conditions of psychological and pedagogical support of forming teachers’ image culture, organization of virtual psychologist workshop activities, application of coach technology in real and virtual environment, dissemination of positive examples and results of the image culture via networking are described.
Management features of the integrated library automation software: The library managers perspectives

The objective of this research paper is to evaluate the different management features of three selected software i.e. NewGenLib, Libsys and SOUL which facilitate the librarian or library managers in their decision making. The study is an attempt to find out the management features in selected library automation software for library managers to know the use pattern of different services and facilities available in LAS. A comprehensive questionnaire was distributed among 123 library professionals of leading academic libraries across the country to measure their views on library software packages. Out of the total questionnaire distributed, 105(85.36%) library professionals responded with a dully filled-up were received. The consolidated score of different features of selected software was enumerated and responses from participants were tabulated to compare the management features in different software. The ranking of software based on the availability of management features in the selected software has also been evaluated.
A Validation Study Involving Development of Model by Application of Artificial Intelligence in Screening and Prognosis of Breast Cancer

Introduction: Breast Cancer (BC) is a worldwide prevalent carcinoma with varying growth rates.It is also a heterogeneous condition with several clinical manifestations.There is a guideline for effective screening of BC, but the mammograms that are obtained for the same often return false negatives or may not effectively diagnose BC, especially at the early stages.The mortality rates due to BC is also significant and efficient screening may reduce more than 30% of mortality.The assessment and accurate determination of tumour growth is difficult due to the substantial failures of screening BC cases.Aims and Objectives: This current study intends to introduce methods of screening BC using an Artificial Intelligence capable machine learning system, mainly for accurate and efficient detection of BC and assessing its prognostic outcomes.Materials and Methods: The study comprises a cohort of 120 BC patients aged between 50 and 65 years old.The study involves the development of serial mammograms for detection of in-vivo growth rate and further assessment of immunohistochemical staining.The study also facilitates the development of a machine learning model for detecting BC cases from serial mammograms and several other factors.Statistical analyses have been conducted for effective significance tests between the newly developed and existing methods.Results: The newly developed method proved to be successful in accurately assessing several aspects of the tumour, including size, stage, grade, mitotic score, type of histology, Nottinham Prognostic Index (NPI) and many more.The newly developed method is more significant than the existing one in assessing clinico-pathological characteristics.The newly developed method has shown significantly better prognostic ability than the existing one (p = 0.0284).Kaplan-Meier Survival Curve has been plotted to analyze the growth of the tumour with its prognosis. Conclusion:The study has effectively presented the development of a prognostic model for Breast Cancer patients with the application of machine learning algorithms, which can be utilized in the screening of breast cancers and proper management of the same to improve its prognosis.
Driving Waveform Dependency of Energy Dissipation of Trapped Particles

Here, we present a numerical simulation of the trapping of particles in a Paul trap to analyse the impact of exotic driving waveforms to disclose the trapping dynamic and maximize the energy dissipation for applied additional cooling techniques.
A Declarative Domain Model Can Serve as Design Document

Detailed design documents have been criticized as a hard to maintain artifacts that may easily become useless while a game under development keeps evolving. In this paper we propose the use of declarative domain modelling as a communication tool and a form of contract between designers and programmers. We show how this model, including entities and actions relevant for the game design, can also serve to support debugging tools for game designers.
I MPLEMENTATION AND PERSPECTIVES OF CAREER DIGITAL PLATFORM’ DEVELOPMENT IN HIGHER EDUCATIONAL INSTITUTIONS

This article represents the results of an analysis of in-depth interviews conducted with of the Higher School of Economics – St. Petersburg and St. Petersburg State Agrarian University about the existing problems in universities in the field of student career development, tools for solving these problems and expectations from a digital career platform as an example of a solution. The purpose of the research is to identify the causes of pain of students of the previously listed St. Petersburg universities related to career development during their studies, which need to be solved, for example, with the help of a digital career platform. During in-depth interviews, through one-on-one communication in social networks, the needs of students related to their career formation, the necessity of informing about career opportunities and the use of digital tools for career development were identified. The results obtained indicate the need to create a digital career platform in universities aimed at supporting student organizations, career events, publishing internships, vacancies, partner company news and career advice, integration into the performance monitoring system. In addition, the article carries out a market assessment and a financial plan for a digital career platform.
Use of web 2.0 applications on Indian University library websites

The main objective of this research work is to examine the adaption of the Web 2.0 application in the libraries of Indian university. The study mainly emphasises the various Web 2.0 tools and their purpose and features on the select library websites. In terms of a quantitative technique, content analysis was conducted. For data collection, a checklist was created based on prior checklists and questionnaires from similar studies, as well as synthesised concepts from the reviewed literature. Then all selected university libraries websites were accessed by the researcher for data collection. The research result shows that Eight Indian university libraries used at least one or more Web 2.0 tools, and five Web 2.0 tools (SNS, RSS, Blogs, Wikis, and Vodcast) were utilised for specific purposes with characteristics. The selected university Web 2.0 application indexes have been observed low.
Research on Vision/Radar Sensor Fusion Positioning Algorithm Based on Strong Tracking Filter and Unscented Kalman Filter

The sudden change of vehicle driving environment will reduce the positioning accuracy of single sensor for target vehicle. In this paper, strong tracking unscented Kalman filter (STUKF) algorithm is used to fuse vision and radar sensors to realize multi-sensor fusion positioning of target vehicle and improve positioning accuracy. Firstly, the vehicle motion model is established, and the unscented Kalman filter (UKF) algorithm is studied. In order to improve the robustness of the UKF algorithm, a strong tracking filter (STF) is introduced into the UKF calculation process, and the fading factor is used to correct the prior error covariance in real time to improve the positioning accuracy of the target vehicle. Finally, simulation verifies that the proposed algorithm has good positioning accuracy and robustness under strong transient conditions.
Access to and Utilization of Information and Communication Technology by the Teaching Staff at Tishk International University

The objective of this study is to assess the usage of Information and Communication Technologies (ICT) by TISHK International University (TIU) teaching staff.The data were obtained from 62 teaching staff from the Engineering, Information Technology (IT) and Education faculties.A standardized questionnaire was utilized to obtain data from research participants.Descriptive statistics, Confidence Interval, One-Sample t-test, Two-Sample t-test, and one-way ANOVA were all used to analyse the data obtained.This facilitated the evaluation of differences in usage by various departments and faculties at the university.The outcome of the study was that there were no differences in usage of ICT regarding the participants' educational status, ages, gender, educational level, and marital status.However, a significant difference in the number of hours spent using ICT tools was found.
A Study on the Strategies of Integrating Traditional Culture into College English Teaching

Traditional Chinese culture is the crystallization of the wisdom of the Chinese nation and an important way to enhance China's international image and expand China's international influence.In the current college English teaching, the teaching content and form are single, and the traditional cultural elements are less reflected.Teachers lack knowledge of traditional culture and cannot tell Chinese stories well in English.Students are easy to be influenced by foreign culture, and their awareness of traditional culture is not strong.In order to improve the students' culture communication ability, this paper discusses the form of integrating traditional culture into College English teaching and proposes the strategy of integrating the first classroom, the second classroom and the third classroom.It integrates traditional culture into College English teaching in an all-round and multi-angle way and provides new thinking for integrating traditional culture into College English teaching.
A new model view controller implementation to maximise maintainability of web-based applications

The development of web applications has known a very high evolution during the last years. Architecture software developers apply model view controller (MVC) architectural patterns to develop web-based applications. Certainly, quantitative evaluation of web-based applications regarding quality attributes is very recent and important. This paper aims at proposing a new architecture of the MVC pattern to enhance the quality of web applications in some capacity such as system of systems domain. It focuses on the maintainability quality attribute. It presents a methodical refinement and mapping of the maintainability to a set of metrics for good design. Our results have been developed and evaluated in a series of applications based on a special tool called Code Metrics.
Multi-level control for mobile robot systems *

The paper contributes with a hybrid controller for multiple mobile robot systems (MMRS) that ensures the correct accomplishment of its mission by each robot. We capitalize on earlier results concerning the supervision of the robot flow, in particular the concept of partitioning the motion area into cells. Then the system comprises three control levels: DES (Discrete Event System) based cell-transition controller, APF (Artificial Potential Field) based cell-traverse controller, and CTS (Continuous Time System) based robot motion controller. The breakdown of the coordination concept into two control levels allows the optimal solution of the system liveness problem, and the holistic approach to the control of MMRS provides for its direct implementation in both a simulator and a real-robot test-bed.
Hybridization of ML techniques for predicting Breast Cancer

Breast cancer is one of the most prevalent forms of cancer among Indian residents. Breast cancer ranks fourth in the top ten cancers in America. Every four minutes, a woman in India is diagnosed with breast cancer, according to the statistics. Women in rural and urban India are more likely to get breast cancer than in the past. One in twenty-eight Indian women will be diagnosed with breast cancer. Urban women are more likely to suffer from it (1 in 22) than rural women (1 in 60). According to breast cancer statistics from 2018, there were 1,62,468 newly reported cases and 87,090 fatalities. These deaths can be avoided if cancerous cells are detected early. This research describes a strategy for detecting breast cancer using ML techniques. The primary goal is to predict breast cancer from the benchmarked input dataset, which consists of the information about Benign and Malignant based on symptoms. The system is built using two Algorithms Logistic Regression and Decision Tree. The obtained accuracy of the proposed method was 96.8%, whereas the precision score were found to be 94.5%.
Awareness of Wrong-Lane Accident Detection using Random Forest Compared with SVM Algorithm with Increased Accuracy

Aim:The proposed study aims to perform detection of wrong-lane accidents utilizing the Support Vector Machine (SVM) algorithm and compare accuracy with the Random Forest (RF) algorithm.Materials and Methods: Support Vector Machine is applied on a road accident dataset that consists of 1834 records.A machine learning strategy for detecting wrong-lane accidents has been suggested and developed that compares Support Vector Machine with Random Forest.Sample size was calculated as 21 in each group using G power.Sample size was calculated using clinical analysis, with alpha and beta values of 0, 05 and 0.5, 95% confidence, 80% pre-test power and enrolment ratio is 1.The accuracy of the detection of wrong-lane accidents was evaluated and recorded.Results: The accuracy was maximum in detection of wrong-lane accidents using Support Vector Machine (89.90%) with minimum mean error when compared with Random Forest (89.77%) and attained significance value of p = 0.02.Conclusion: The study proves that Support Vector Machine Algorithm exhibits better accuracy than Random Forest in detection of wrong-lane accidents .
Face Morph Detection: A Systematic Review

Biometric verification especially faces recognition has been widely applied in daily life, including in immigration. Face recognition is used for identification verification in passport identities. But there is a security threat in face recognition, which is called face morph. Face morph is the combination of the passport applicant's face image with the face image of another subject to produce a face image similar to the face of the applicant's passport document by covering the facial features of the other subject. It can cause serious problems if misused by people listed on the blacklist. Blacklists are people who are subject to immigration restrictions such as suspension of passport issuance, inclusion on the prevention list, and inclusion on the deterrence list. So that the person listed on the blacklist can have a passport to then cross the border out of Indonesian territory which can cause problems and disturb the country's sovereignty. This research uses the Systematic Literature Review (SLR) method to collect and analyze previous studies related to the research topic. The result of this research is to get the method and trend of face morph detection. Detection of face morphs is generally divided into 2 scenarios, i.e., single image-based and differential image-based. The detection method for single image-based is divided into texture, quality, noise, deep learning, and hybrid, while the detection method for differential image-based is divided into feature difference and demorphing. So, with face morph detection, it is expected to be able to increase the security of passport documents and increase security at immigration checkpoints.
Compensation of linear inter-mode cross talk in multiplexed continuous-variable quantum communication

We analyze degradation of multimode entanglement and quantum key distribution in the presence of cross talk. As countermeasures we suggest optimization of initial entangled resource or cross talk compensation by additional coupling between the modes.
The Visualization Approach Based on Data Flow for Traffic Trajectory

To create traffic trajectory visualization through visual graphics interactively, the traffic trajectory-oriented data flow visualization method is proposed. The method uses wizard recommendations to assist in data flow construction for traffic track clustering, correlation analysis and anomaly detection. Firstly, the data flow system is developed to interactively create traffic trajectory visualization. Secondly, the wizard recommendation model based on heterogeneous networks of data flow is proposed to assist in data flow construction. The recommendation model treats wizard recommendations as a graph search problem and feeds user ratings on wizard recommendations to a heterogeneous network of data flow to update node relationships. The results of the case evaluation based on real traffic trajectory data and the evaluation of the Likert scale on users' propensity to choose indicate the validity of the approach.
Effect of using Flashcard and Blackboard in learning Vocabulary among Students with Hearing Impairment

The purpose of this study was an attempt to examine the effect of using Flashcard and Blackboard in learning vocabulary among students with hearing impairment.The study was an experimental method using pre-test and post-test design.The sample of the study was 30 students studying from 1 st to 5 th standard in two schools for the hearing impairment, and the sample was chosen through stratified random sampling method.The tools developed by the researcher and used in this research was a Teacher Made Test contains 11sight words.The students were taught using traditional classroom teaching method.To analyze the collected data, Repeated Measures of ANOVA was used.The Test analysis reveals significant improvement in learning vocabulary through flashcard and Blackboard teaching.The study reveals the efficacy of using flashcards and blackboard teaching strategy in learning vocabulary among students with hearing impairment.
Text Mining Approaches for Dependent Bug Report Assembly and Severity Prediction

In general, most existing bug report studies focus only on solving a single specific issue. Considering of multiple issues at one is required for a more complete and comprehensive process of bug fixing. We took up this challenge and proposed a method to analyze two issues of bug reports based on text mining techniques. Firstly, dependent bug reports are assembled into an individual cluster and then the bug reports in each cluster are analyzed for their severity. The method of dependent bug report assembly is experimented with threshold-based similarity analysis. Cosine similarity and BM25 are compared with term frequency (tf) weighting to obtain the most appropriate method. Meanwhile, four classification algorithms namely Random Forest (RF), Support Vector Machines (SVM) with the RBF kernel function, Multinomial Naïve Bayes (MNB), and k-Nearest Neighbor (k-NN) are utilized to model the bug severity predictor with four term weighting schemes, i.e., tf, term frequency-inverse document frequency (tf-idf), term frequency-inverse class frequency (tf-icf), and term frequency-inverse gravity moment (tf-igm). After the experimentation process, BM25 was found to be the most appropriate for dependent bug report assemblage, while for severity prediction using tf-icf weighting on the RF method yielded the best performance value.