Reviewerly/exHarmony huggingface dataset:
Assessment of the level of scientific interaction between pedagogical universities and educational development institutions

Introduction. Pedagogical universities and educational development institutes (EDIs) have a single activity space. However, the interaction in the scientific field between these educational institutions currently needs to be improved, and this requires an assessment of the current level of interaction. The research purpose is to analyze the current level of scientific interaction between pedagogical universities and EDIs. Materials and methods. The level of interaction between organizations was assessed in accordance with the model by L. Borrell-Damian et al. by three parameters: 1) the content of joint activities; 2) legal aspects of interaction; 3) the main forms of communication. The study involved EDIs and pedagogical universities subordinate to the Ministry of Education of the Russian Federation from 46 regions of Russia (35 EDIs and 20 pedagogical universities), which representatives filled out a questionnaire using Google Forms. Results. The study shows that in scientific interaction, educational institutions primarily use its short-term options, among which scientific-practical conferences were most often indicated (70% of pedagogical universities and 74.3% of EDIs). Long-term options for scientific interaction are used much less often than short-term ones (the differences are significant for p≤0.0001, χ2EDIs=32.08, χ2pedagogical universities=1.54). The availability of a network interaction agreement was indicated by 45.7% of representatives of the EDIs and 25% of the pedagogical universities. Moreover, joint activities in scientific work plans is indicated by a significantly larger number of respondents: 50% of the pedagogical universities and 68.6% of the EDIs. The main communication option in the scientific sphere is an informal one, which is based on direct personal contacts (70% of the pedagogical universities and 77.1% of the EDIs). Formal communications are two times less common, the differences are statistically significant (χ2EDIs=20.63, χ2pedagogical universities=14.4, p≤0.0001). Conclusion. The current system of scientific interaction between pedagogical universities and EDIs may be assigned to the project level, since it is built on informal personal communications, focused on individual scientific projects with limited implementation periods, which are implemented with the execution of short-term contracts. Raising the level of partnership interaction up to a strategic one will increase the productivity and efficiency of scientific partnership.
Designing Framework of Cloud Health Record (CHR) System for Patient Health Information Storage: A New Prophecy

Patient health record system offers significant health care benefits to improve the quality of health amenities, it has been reducing alarm rate of failures in medicinal treatment.Medical records contain large volumes of aggregated and longitudinal clinical data that could insist patient safety.In order to minimize the cost, replication of records and infrastructure, the emerging technologies like server virtualization and cloud computing is unavoidable at present medical environment.Cloud technology facilitates data storage, that keeps for all time availability of every individual patient information.Storing and maintaining the patient information is very essential for the future reference by any other hospitals and medical experts.Cloud computing makes convenient to the other stake holders of healthcare environment such as pharmacist, nurses, lab technicians, physician and other specialists.Existing patient's health records (PHR) maintained by manually and electronically (EHR) and stored in localized server controlled by hospital authority.In this article, we outlined new prophecy of Cloud based storing system called Cloud Health Record (CHR), we suggest that the new system will help to store huge volume of patient information and it will facilitate sharing the patient records on the cloud based system for getting further and better opinion from the physician, who lives anywhere.
An Augmented Reality Learning Environment for Informal Geoinformatics Education

In this paper, we describe an interactive learning environment for geoinformatics enriched by Augmented Reality (AR) that was designed by researchers and practitioners using a design-based research approach.In total, four AR modules were developed to engage learners physically, cognitively and emotionally in the learning process.AR is thus used not merely as a visualization technology, but also to initiate and scaffold learning activities.To address the known challenges of learning with AR, we designed our learning environment to be used by teams of students.This can counteract the problem of cognitive overload, or frustration due to technical problems.We examined our technological and pedagogical framework in an evaluation with 12 learners.The results show that our design did not lead to cognitive overload; technological aspects, such as usability, were also evaluated positively.However, we also detected possible improvements to our design, such as using AR glasses to free the hands for task completion.Overall, it was shown that AR is a good way to expand traditional learning opportunities with digital ones and to enable holistic learning experiences.
Evaluating The Quality of The Indonesia-English Translation of Research Abstracts Written By The Students Of Harapan Bangsa University

This study was focused on evaluating the quality of the Indonesian-English translation of the research abstract written by the students of Harapan Bangsa University. The analysis included the analysis of translation quality in terms of accuracy, naturalness, acceptability, and readability as well as the translation ideology. The results of the study showed that the accuracy of the research abstract translation was dominated by highly accurate translation and less accurate translation in the target language which shared the same percentage as many as 36%. In terms of naturalness, it showed that 34% of the translation was categorized into highly natural. With regard to translation acceptability, it showed that 61% of the data belonged to acceptable. Meanwhile, in terms of translation readability, it showed that 75% of the data was categorized as readable. The register and genre of the research abstract texts and its translation were kept equivalent and holding the same purposes as it was translated overtly. In translating the research abstract text, it is suggested that the translator could highly consider the accuracy, naturalness, acceptability, and readability of the translation, particularly for target readers. With regard to the translation ideology, the translator is suggested to grasp the register and genre of the text before the translation process and regard the target readers to determine whether the text should be translated covertly or overtly.
A workflow for defining geological domains using machine learning and geostatistics

Determining geological domains to be modeled is one of the first steps in the mineral resource evaluation process. Prior knowledge regarding the geology of the deposit is fundamental but, in most cases, not enough for a reasonable definition of these domains. A careful statistical analysis of the available data (e.g. geochemical samples) is also of great importance. In order to avoid mixing different populations of data, samples with similar characteristics should be grouped together. In the context of supervised machine learning, cluster analysis can be especially suited for this matter and there are many different algorithms available in the literature. In this paper, two clustering techniques were investigated: the first is the k-means algorithm, one of the most widely used methods in machine learning, based on the iterative analysis of the statistical distribution, while the other one is based on spatial autocorrelation statistics, which takes into consideration the geographic distribution of samples. The choice of the most appropriate technique, as well as the number of domains can be challenging when performing cluster analysis, and the evaluation of an expert is still necessary, as the results are subjective.
Improving Students’ Oral Communication Skills in Public Speaking through Individual Presentation Task

This current study attempts to seek the answers of a question: how effective is the individual presentation method in improving students' oral communication skills, especially in public speaking? Designed as a qualitative research, this research was conducted at the Faculty of Tarbiyah and Teacher Training of IAIN Pekalongan in the academic year of 2019/2020. The research involved 100 of the second year students of the English Education Department of IAIN Pekalongan who took Public Speaking course. The data were collected through an open-ended questionnaire as the instruments of this study to get their perception after taking the course with performing an individual presentation as its main task. The research findings reveal despite the obstacles they encountered, the presentation task was effective to enhance students’ confidence as well as to give them experience to speak in front of a crowd. The task also gave them more awareness and self-evaluation on how to perform a good public speaking as they received direct feedback from the audience while performing the individual presentation.
Analysis and Estimation of Interaction and Reusability Complexities of Components in Component-Based Software Engineering

Reuse is the elementary and essential attribute of the component-based software engineering. Reusability focuses on building huge and complex software through assembling pre-fabricated software constructs. In this paper, our emphasis is on to analyze and quantify the two core notions of component-based software, that is, reusability and interaction behavior of the components with the goal of minimum complexity generation. We analyse prominent works available in the literature in the area of reuse and interaction complexities. We Analyze and Estimate reusability complexities of components in Component-Based Software Engineering and we propose efficient and useful metrics to compute the reusability of components. Reusability metrics are defined for individual components as well as overall system level. Different types of components are defined on the basis of their reusability, that is, components that can be reused as it as, and tailored components. To define the metrics we have used lines-of-code as the basic parameter due to its simplicity and countability. We proposed a matrics which select the best component on the basis LOC based complexity.We compare IRMCcTotal-i matrics with SCCp metrics and SCCr metrics and analyses that negative relationship between SCCp and IRMCcTotal-i shows low portability and positive relationship between SCCr and IRMCcTotal-i shows high portability.
Feature Extraction based Breast Cancer Detection using WPSO with CNN

The cancer reports of the past few years in India says that 30% cases have breast cancer and moreover it may increase in near future.It is added that in every two minutes, one woman is diagnosed and one expires in every nine minutes.Early diagnosis of cancer saves the lives of the individuals affected.To detect breast cancer in early stages, micro calcifications is considered as one key symptom.Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used.Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment.This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying micro calcifications as normal, benign or malignant thereby improving the accuracy.In the breast region, tumour part is extracted using optimization methods.Here, Convolutional Neural Networks (CNNs) is proposed for detecting breast cancer which reduces the manual overheads.CNN framework is constructed for extracting features efficiently.This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal.This model uses MG images which were obtained from various local hospitals.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CNN UNTUK SISTEM PENGENALAN WAJAH

Face recognition system is generally divided into two stages, face detection system, which is a pre-processing step followed by a facial recognition system. This step will quickly be done by humans but it takes a long time for the computer. This ability of humans is what researchers want to duplicate in the last few years as biometric technology in computer vision to create a model of face recognition in computer. Deep learning becomes a spotlight in developing machine learning, the reason because deep learning has reached an extraordinary result in computer vision. Based on that, the author came up with an idea to create a face recognition system by implementing deep learning using the CNN method and applying library open face. The result of this research is applying deep learning with the CNN method to classification process that resulting percentage of precision of 96%, recall percentage of 100%, and accuracy percentage of 99.8%.
Fund Concessional Financial Support For Low-Income Countries—Responding To The Pandemic

Redistribution 3 countries falling into debt distress.Higher levels of lending would mean higher credit risk to the Fund and a corresponding need for more in-depth analysis of capacity to repay the Fund.Directors supported the proposal to give enhanced attention to debt dynamics and capacity to repay in staff analysis and in program documents, along the lines discussed in Annex VI of the Board paper.In this regard, Directors emphasized the importance of taking into account country-specific circumstances and called for the Fund to
Crowd Analysis System for Images of CCTV Camera

It is important to monitor and analyze the crowd for peaceful event organization and minimum number of persons killed or injured in a stampede, war or accident in public or religious places. Ultimately, crowd management strategies should be adopted for crowd safety. Detecting the abnormal crowd behaviour is the interesting research area. Traditionally to monitor the crowd closed circuit television is used. Video analyses are used in many application areas. This work is a paper of traditional approach and is a study of different attributes of crowd which include crowd counting and estimation of density, detection of crowd motion. It also presents the study about behaviour understanding of crowd and crowd tracking. We have also presents crowd analysis using different deep learning methods. This paper showed results of crowd analysis using convolutional neural network and different recurrent neural network training model.
Fault Model for UML Behavioral Activity and Sequence D iagrams

In software testing, the fault detection in any software construct is very important factor to check how efficiently testing process is carried out. While testing software, it is required to take some coverage criteria to check the testing methodology. The paper shows a way for fault detection for UML behavioral diagrams. Different types of faults which can occur in UML diagrams are discussed and a fault model is proposed for combinational diagram made by integrating UML behavioral diagram such as activity and sequence diagrams. The percentage of fault detected in software is calculated using fault model and to prove how efficient is the software testing process.
Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods

Among many film streaming platforms that have sprung up, Netflix is ​​the platform that has the most subscribers compared to the other platforms. However, not all reviews provided by the Netflix users are good reviews. These reviews will later be analyzed to determine what aspects are reviewed by the users based on reviews written on the Google Play Store, using the Latent Dirichlet Allocation (LDA) method. Then, the classification process using the Support Vector Machine (SVM) method will be carried out to determine whether each of these reviews is included in the positive or negative class (Sentiment Analysis). There are 2 scenarios that were carried out in this study. The first scenario resulted that the best number of LDA topics to be used is 40, and the second scenario resulted that the use of filtering process in the preprocessing stage reduces the score of the f1-score. Thus, this study resulted in the best performance score on LDA and SVM testing with 40 topics, and without running the filtering process with the score of 78.15%.
The Virtual Doctor Is In: The Effect of Telehealth Visits on Patient Experience

COVID-19 has accelerated the adoption of telehealth.With this shift comes a need for empirically based research regarding the effect of telehealth on patient experience.The present study employed an online survey (N = 996) examining whether a patient's perceptions of a telehealth visit predicts (a) the likelihood that they will schedule a future telehealth visit, and (b) their recall of clinical information.Participants viewed a video of a real clinician delivering information on a COVID-19 antibody test, and responded to demographic, socioemotional, and cognitive items.We found that for every 1-point increase in an individual's satisfaction with their interaction with the doctor, they were .73times more likely to revisit the doctor (p < .01).These results provide insight for researchers and medical professionals regarding patient perceptions of virtual encounters and suggest best practices to consider as we further integrate telehealth.
Synthesis and Visualization of Image Datasets of Parametric 3D Model for Neural Network Training and Testing in Data-Poor Conditions

Flaw detection of the inner surfaces of especially critical pipes is based on visualization, which requires complex optical systems equipped with artificial intelligence functions.Training of such systems is very complicated due to the limited volume of defective products.The paper describes training and testing of machine learning algorithms in poor data by the example of detecting a defect at the inner surface of a pipe.The authors propose a method for developing a set of synthetic training images obtained using 3D models of technical objects with parameterized defects applied to them.Images can be generated by parametric description of the artificially defected inner surface of a pipe 3D model in Autodesk Inventor environment.Windows AutoIt OS automation environment is applicable to generate synthetic images and masks.The method allows obtaining a set of synthetic pipe images for training neural networks with U-Net and LinkNet architectures.The trained neural networks testing has shown the defect recognition at a high level both on a synthetic sample of images and on real images of inner surface of rejected pipes.
Kemampuan Menulis Teks Cerita pada Mahasiswa Program Sarjana Pendidikan Bahasa Indonesia Fakultas Keguruan dan Ilmu Pendidikan Universitas Bengkulu

The purpose of this study is to describe the ability to write story texts for students of the Indonesian Language Education Undergraduate Program FKIP Bengkulu University. The scope of this research covers aspects of (1) the suitability of the theme with the content, (2) the structure of the story text, (3) the choice of words (diction), (4) the use of story-style sentences, and (5) the Indonesian Language spelling and handwriting. This research uses descriptive method and quantitative approach. The research population was all students of the Indonesian Language Education Study Program FKIP Bengkulu University in the odd semester of 2018/2019 and the sample was purposive sampling, determining that there were 34 students in semester III-A. Collecting data uses test techniques and instruments in the form of instructions for writing stories with the theme of "love" and guidelines for assessing the ability to write stories based on five components, including content, text structure, diction, sentences, and mechanics. Data analysis used descriptive statistics to find the mean and determined qualitative results. The results showed that the ability to write story texts for the students of the Indonesian Language Education Undergraduate Program, FKIP Bengkulu University, was categorized as good. This is based on the score of all components with an average of 73.97. These results still require efforts to improve to be better. The results of each aspect of the ability to write story texts, namely aspects (1) the suitability of the theme with the content, good category (18.94), (2) story structure, good category (16.09), (3) accuracy of diction in good category (score 14 ,3), (4) narrative language style sentences are in good category (17.24), and (5) mechanics (BI spelling and handwriting) are in good category (7.32).
Data Sharing Network Model and Mechanism of Power Internet of Things in Virtualized Environment

In order to solve the problems of low security and low reliability in power Internet of Things data sharing in a multi-domain environment, this paper proposes a power Internet of Things data sharing network model and mechanism in a virtualized environment.The main contributions of this paper include building a data management and control network model and a trust transfer relationship model, proposing a heuristic data transmission routing strategy and a network node credit evaluation mechanism.In terms of the data management and control network model, this paper proposes a data sharing network model for multiple domains.The model includes data usage nodes, data transmission nodes, data storage nodes, and data sharing coordination nodes.In terms of network congestion for data sharing, this paper proposes a heuristic data transmission routing strategy.This strategy analyzes the resource utilization of the bottom node and the bottom link, and realizes the load balance between the bottom node and the bottom link.In terms of data sharing authority management, this paper proposes a trust transfer relationship model.This model can solve the security problems of different security level domains when crossing authentication, and realize the transmission and evaluation of the credibility of different IoT systems.In terms of the identification and management of malicious nodes for data sharing, this paper proposes a credit evaluation mechanism for network nodes.This mechanism can solve the problem of malicious nodes causing data unavailability or data sharing routing resource allocation failure.In the performance analysis section, the algorithm in this paper is compared with the traditional algorithm, and it is verified that the algorithm in this paper has improved the security and reliability of the data in the data sharing process.
Key frame and skeleton extraction for deep learning-based human action recognition

In this paper, we propose an efficient approach for activity recognition in videos with key frame extraction and deep learning architectures, named KFSENet. First, we propose a key frame selection technique in a motion sequence of 2D frames based on gradient of optical flow to select the most important frames which characterize different actions. From these frames, we extract key points using pose estimation techniques and employ them further in an efficient Deep learning network to learn the action model. In this way, the proposed method be able to remove insignificant frames and decrease the length of the motion vector. We only consider the remaining essential informative frames in the process of action recognition, thus the proposed method is sufficiently fast and robust. We evaluate the proposed method intensively on public dataset named UCF Sport and our self-built HNH dataset in our experiments. We verify that our proposed algorithm receive state-of-the-art on these datasets.