Reviewerly/exHarmony huggingface dataset:
Final State Certification of Biprofessional Educational Programs Graduates in Russia: Building a Model

The paper aims to improve the diagnostic procedures of the final state certification of university graduates studying the biprofessional content training program. The research purpose is to build and test a final state certification model of biprofessional educational programs graduates. The authors propose a functional final state certification model of biprofessional educational program graduates, which includes five blocks: (1) normative, (2) informative, (3) technological, (4) diagnostic, and (5) organizational-managerial. A graduate’s competencies, the integrity and effectiveness of their training as a social procurement requirement are confirmed during the final state certification. Certification is a system-forming factor. The paper presents the state examination effectiveness results using automated control in complex interdisciplinary testing and competence-oriented tasks. The proposed model allows for more accurate and targeted diagnostics of the graduates’ educational achievements at state examination during the final state certification.
Cluster as the Basis for Creating an Innovative Educational Product

The article reveals the basics of the strategy of modernization of the education system. At the present stage of its reform, they are aimed at increasing the competitiveness of educational institutions in the educational services market, at creating maximum accessibility and openness of education for the population and business. They should be carried out within the framework of innovations, taking into account the main directions of the socio-economic development of the republic, the implementation of priority areas of state policy in the field of education.
Technological Support for the Cultural Training System for Students at Technical Universities

The paper studies the trends of higher technical education development and establishes the relationship between education and culture as one of the conditions for solving the problems of modern society. The authors determine that one of the main problems that hinder the formation of the culture of the future engineer is the lack of a reasonable selection of cultural elements. The presence of such elements ensures the full development of the specialist. The most generalized socio-cultural components of culture are (1) the sphere of material production, (2) the cultural sphere, and (3) the sphere of communication. The authors determine the components of the culturological training content as the implementation of the support system of the integrated professional, universal cultural, and moral cultural development of technical university students. It is experimentally proved that the implementation of a cultural training system of a future engineer leads to statistically significant changes in the levels of knowledge acquisition, the formation of skills and qualities of a future specialist.
Graph Convolution Network Recommender Rystem with Feature Embedding

Recommender system has become one of the most important parts of information services on the Internet today the model based on graph neural network has been proved to be a very effective method in collaborative filtering recommender system [1,15]. However, the recommender system based on graph network usually only uses the interactive relationship between users and items, and their feature information is often not used effectively. In this work, we combine the use of feature information with graph convolutional network to design a new recommendation algorithm, Graph Convolution Network recommender system with Feature Embedding, GCNFE. Tests on the Movie lens and Last-FM datasets show that the new model performs better than previous models in most cases.
An Evaluative Study of Decentralization Practices of Head Teachers in District Kech Balochistan

The purpose of the study was to evaluate the decentralization practices of head teacher at District Kech Balochistan. The nature of the study was quantitative and survey type. The population is comprised of 78 of which 45 Boys and 30 Girls’ Secondary Schools, DEO Male, DOE Female and Divisional Director School. There are total 78 Girls and Boys Secondary School in Kech. Pilot testing was done in 10 schools out of 78. Questionnaire was used as research tool. The questionnaire was comprised of closed-ended questions. The reliability of these tools were calculated through SPSS (Version, 22). After finalizing and pilot testing, the questionnaire was distributed to the respondent for collecting data. The questionnaires were served through postage, email by hand in face-to-face meeting by the researcher to the respondent. The questionnaire was scrutinized for errors and omission, ambiguity and relevance. The reliability was calculated through SPSS (Version, 22). After finalizing and pilot testing from 10 head teachers out of 75, the questionnaire were distributed among respondents for collecting data. After their feedback and views, questionnaires were improved. The data were obtained through questionnaires and analyzed by using, Chi square, percentage and mean.&#x0D;
SmartReviews: Towards Human- and Machine-actionable Reviews

Review articles summarize state-of-the-art work and provide a means to organize the growing number of scholarly publications. However, the current review method and publication mechanisms hinder the impact review articles can potentially have. Among other limitations, reviews only provide a snapshot of the current literature and are generally not readable by machines. In this work, we identify the weaknesses of the current review method. Afterwards, we present the SmartReview approach addressing those weaknesses. The approach pushes towards semantic community-maintained review articles. At the core of our approach, knowledge graphs are employed to make articles more machine-actionable and maintainable.
A context-aware unsupervised predictive maintenance solution for fleet management

Abstract We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.
Process Tracing and Narrative Science

Process tracing is a familiar analytical tool in a number of sciences. Successful process tracing pulls together what is already known, believed or assumed and the various events, activities and entities in a case study in order to construct a narrative of the case. Several chapters in this volume offer accounts of narrative science that are explored through process tracing. These examples are analysed to reveal how various aspects of process tracing inform narrative and how narrative, in turn, aids process tracing in an iterative process of interpretation and reinterpretation of evidence, testing, development and revision of hypotheses, and the explanation of singular events.
Reasoning from Narratives and Models: Reconstructing the Tohoku Earthquake

This chapter examines the role of three kinds of narratives in producing knowledge about the rupture process of the Tohoku earthquake of 2011. I show that each of the three kinds of narratives appears in one of three stages on the way from data recorded of the earthquake to a reconstruction of the rupture process. In the first stage, rupture narratives are produced by computational tools called source models. In the second stage, a set of details that is taken accurately to represent features of the actual rupture process is distilled out of these conflicting rupture narratives through the use of a ‘research narrative’. In the third stage, these distilled details are strung together into an integrating narrative. This integrating narrative is used as a research tool for formulating questions, the pursuit of which has led to the production of further evidence about the rupture process.
O CANTO TERAPÊUTICO COMO ESTRATÉGIA DE COMUNICAÇÃO E PROMOÇÃO DA SAÚDE: relato de experiência

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Implementation of the infection control estimate: A case study on the use of a newly developed digital tool for outbreak management in the acute setting

An Infection Control Estimate (ICE) Tool was developed based on a previously published concept of applying military planning techniques to Infection Prevention and Control (IPC) management strategies in the acute healthcare setting.Initial testing of the outbreak management tool was undertaken in a large acute hospital in the North-West of England during a localised outbreak of COVID-19. The tool, developed using Microsoft Excel, was completed by trained IPC practitioners in real-time to log outbreak details, assign and manage meeting actions and to generate surveillance data.The ICE tool was utilised across five outbreak control meetings to identify and allocate tasks to members of the outbreak control team and to monitor progress. Within the meetings, the tool was used primarily by the trained IPC Specialist Nurses who were guided by and entered data into the relevant sections. Feedback indicated that the tool was easy to use and useful as the sole repository of outbreak information and data. Suggested improvements following the testing period were made and additional functionality was added.Utilisation of the ICE tool has the potential to improve our understanding of the efficacy of currently employed outbreak management interventions and provides a cognitive support and targeted education for teams responsible for the management of outbreaks. It is hoped that by guiding teams through an outbreak with prompts and guidance, as well as facilitating collection and presentation of surveillance data, outbreaks will be resolved sooner and risks to patients will be reduced.
Multi-source Information Fusion Based Attributed Heterogeneous Network Embedding

Abstract Attributed heterogeneous information network embedding is an emerging research topic in the field of network embedding. It can not only embed nodes with complex heterogeneous information into a low-dimensional space, but also preserve the rich attributed information of nodes. Existing works usually only use single-source information of node attributes or network structure, or simply use some shallow models to fuse multi-source information. These methods will lose the original network information and cannot make full use of the rich multi-source information in the attributed heterogeneous network, thus affecting the expression quality of the network embedding. To solve this problem, we propose a multi-source information fusion based attributed heterogeneous network embedding model (MAHE), which improves embedding performances through the deep fusion of heterogeneous structural information and node attribute information. The MAHE model first calculates the attribute similarity of nodes and the node structure similarity calculated by different meta-paths. Then the node attribute similarity matrix is weighted and fused with multiple heterogeneous similarity matrices to obtain multiple multi-source information similarity matrices. This aims to complement the attribute information with the semantic information of different meta-paths. The autoencoder is then used to learn the embeddings of nodes in each multi-source information similarity matrix. Finally, the attention mechanism is used to fuse these node embeddings to learn the importance of different multi-source embeddings.Experiments on three datasets show the superiority and effectiveness of the MAHE model.
Derivation of a Kalman-type filter for linear systems with pointwise delay in signal noise

Abstract Recently, it was demonstrated that a modification of the Kalman-filtering model with a pointwise delay of the signal noise could improve communication with considerably distanced spacecraft. However, a complete and correct derivation of the equations of the Kalman-type filter for this case has not yet been provided. In this paper, we close this gap. The method of derivation is based on a passage from the distributed delay to pointwise by means of a delta function. The derived equations constitute a system of first-order partial differential equations with the initial and boundary conditions.
Performance analysis towards GUI-based vehicle detection and tracking using YOLOv3 and SORT algorithm

The paper presents a pragmatic approach towards developing and analysing a GUI-based system performance towards detecting and counting tracked multi-type vehicles in mixed traffic conditions using the improved you only look once v3 (YOLOv3) model. It addresses the issue related to accurately localising smaller vehicles in an occluded scenario and environmental conditions. In the training phase of the proposed work stochastic gradient descent (SGD) optimises the network. Further, to detect small vehicles four different scales feature mapping are performed to extract more fine-grained features for accurate detection, followed by concatenating upsampled layers with lower layers to improve the detection performance of low-resolution images. The intersection over union (IoU) approach detects every vehicle in the subsequent frame by assigning a unique ID to classify detected vehicles into five specific classes bus, truck, car, motorbike, and bicycle. Further, the SORT algorithm tracks and counts the detected vehicles. Experimental result on the common objects in context (COCO) dataset shows an improvement in the mean average precision (mAP) by 11% compared to the existing YOLOv3 technique.
DETERMINANTS OF GOOGLE MEET ADOPTION DURING THE CORONAVIRUS PANDEMIC IN VIETNAMESE UNIVERSITIES

The Covid-19 epidemic and its derivatives have had a major impact on many parts of social life, including higher education, general education, and primary education. Institutions and universities must sustain remote operations using digital technologies in order to swiftly adjust to the "new normal," which is regarded one of the generally applicable alternatives. The adoption of Google Meet, Zoom, and Microsoft Teams is extensively used in colleges and universities and is considered a significant milestone in communication. Although the use of online methods is projected to have a beneficial impact, there are still obstacles and hurdles in terms of infrastructure, acceptable new regulations, content, digital content, supporting devices, supporting capabilities, and deployment capabilities in digital environments. The study's aim is to discover the factors that influence the use of Google Meet in the educational process and user satisfaction. The tool's validity and reliability were examined using SPSS analysis for exploratory factor analysis (EFA). Factor-based testing using the Varimax rotation, 39 items remained in the questionnaire. Based on the analysis 378 responses, there are 5 factors affecting the use of the Google Meet tool, with the KMO index measuring the appropriateness of the sampling being 0.967; Bartlett's test shows that the Chi-Square Sig index is 0.000 &lt; 0.05. These findings are guidelines for teachers and educators in identifying the factors driving the more effective use of Google Meet in schools.
CONSTRAINTS OF DIGITAL PLATFORM IN PROMOTING SPEAKING

This study aims to reveal constraint that the lecturer finds while promoting speaking using digital platform for university students. This study is a qualitative study, it investigates an English lecturer discussing how they teach English using Digital Platforms. The investigation was done by observing the online classroom and interviewing the lecturer to strengthen observation. The main data was lecturer interaction with students through Digital Platforms, specifically English Discoveries (ED) and Google Meet. Furthermore, the findings of the study show some constraints that found by the lecturer, there were bad internet connection and lack of signal; lecturer’s and students’ limited knowledge about the technology used and gadget used by the students. These findings suggest the lecturer and students to aware of technology to achieve the goal of the lesson. The lecturer and students should explore more about ED as the additional material. The lecturer also has to consider using digital platforms to teach English communicatively and give students the impression that they are in a real classroom. Regarding to internet connection, the lecturer should discuss with other lecturers along with the institution to consider about providing a short of facilitation to students such as bandwidth or internet quota for free. In addition, undergraduate students were mostly improved their English speaking using digital platform and should find their own methods for taking classes online, such as offering appropriate equipment and a strong signal.
THINK-ALOUD PROTOCOLS ANALYSIS: AN INVESTIGATION OF PARAPHRASING STRATEGIES IN POST-GRADUATE STUDENTS

This research aims to figure out the paraphrasing strategies used by post-graduate English students based on three stages of writing process division: before-writing, while-writing, and after-writing. This research is qualitative study that investigate paraphrasing strategies used by post-graduate English students in writing a thesis. The qualitative data obtained from think-aloud protocols to gather verbal report data on cognitive and metacognitive reading strategies used by the students in producing their paraphrases. For data analysis, the think-aloud protocols were transcribed, and data was coded and categorized relative to the focus of the research. The result shows that students planned for through understanding, read to memorize the most important information before writing a paraphrase. Another, students tended to write from memory with sometimes look at the source text and applied text transformation/substitution strategies while writing a paraphrase. Finally, most students are comparing the text with the source after they have written a paraphrase.
A Review: Face Recognition Techniques using Deep Learning

Face recognition (FR) is one of the most significant types of research that are widely used in various areas, such as finance, preventing crime, protecting the border, and for military purposes. Face recognition is a biometric identification technology based on human facial feature information. There are two main approaches first one is hand-crafted (HC) features which is the traditional method (geometry-based, holistic, feature based, and hybrid methods), and the recent one is based on deep learning (DL). The major purpose of this work to provide UpToDate literature review for face recognition (FR) Techniques. Furthermore, it summarizes the benchmark datasets and the most successful methods used on these datasets for face recognition.&nbsp
The Influence of Multidisciplinary Factor on the Technical Universities' Positions in International Rankings

Abstract : The article is devoted to a comparative analysis of world-class technical universities (TUs) ranks in the International Quacquarelli Symonds (QS) ranking 2021. The top-100 of QS-2021 includes only one Russian TUs, and there are no TUs of large industrial countries such as India and Brazil. Many Russian TUs were founded in the 30s of the last century, were related to certain industry ministries and have retained their narrow orientation up to the present time. However, the manufacturing of modern high-tech products (aircraft, robotics, electronics, medical equipment etc.) requires engineers to have knowledge from various fields of science (multidisciplinarity), supported by profound int erd isc iplina ry r esea rches. In add it ion, multidisciplinary TUs provide the opportunity for students to change their specialty or adjust it during their undergraduate and graduate studies. The analysis showed that leading positions in international rankings are occupied by multidisciplinary research TUs that conduct training and interdisciplinary research at the intersection of various scientificactivities. This study can be useful not only for Russia, but also for the transformation of TUs in other countries. Keywords: multidisciplinary, technical university (TU) , en g i n e e r in g s p e c i a l t i e s , t r a i n i n g , transformation
A meta-analysis of the implementation of ANN back propagation methods in time series data forecasting: Case studies in Indonesia

This research aims to systematically analyze the results of the application of Artificial Neural Network type Back Propagation (ANN-BP) methods in the prediction or forecasting of time series data, case study in Indonesia. Data is collected from the results of the ANN-BP method from indexing databases, namely Google Scholar, DOAJ, and Scopus. From search results by applying eligibility criteria including (1) keywords “prediction, forecasting, ANN Back Propagation, time-series data”, (2) articles published 2011-2021, (3) the amount of data (N), accuracy rate value or correlation coefficient (R), obtained 36 qualified articles. Furthermore, the results of data analysis using JASP software obtained an average ANN-BP accuracy rate of 90% and a coefficient estimate of 0.901 at intervals of 86%-94% with random effect (RE) models. Based on the moderator variables of the year of publication is obtained the information that in the interval of 2013-2015 by 81%, in 2016-2018 by 90%, and in 2019-2021 by 94%. Finally, if the data input is higher, then the better the data pattern recognition by ANN-BP.
Research Publications as a Means of Analysis and Assessment of Research Activity

The chapter describes methods of evaluation of scholarly journals and research activities ofscientists, organizations and countries based on citation analysis. Peculiarities of research pub-lications in different subject areas are explored, as well as possible approaches to comparativecitation analysis in various research fields. Special attention is paid to altmetrics and to the useof bibliometric indicators for ranking of universities and research institutions.
Application of the Aesthetic of Guqin (古琴) for Contemporary Music Composition

The purpose of this research is to discover the application of aesthetic approach, performing technique and playing style of Guqin for contemporary music composition. Mentioned in documents dating from two millennia, Guqin is one of the oldest Chinese solo instruments. Moreover, Guqin music often is based on senza misurato (散板, without tempo) and it focuses on changing the colours of several notes, occasionally even a single note. This is accomplished by using an open string note (散音), stopped string note (按 音), harmonic note (泛音) as well as four main ornaments (綽注吟猱) to achieve the poetic mood. This asthetic is in direct contrast to western homophonic structure. These characteristic effects offer an attractive potential for their application to new compositional techniques and approach to contemporary music writing. This research will provide valuable information regarding employment of the Guqin’s philosophy for music composition.