Reviewerly/exHarmony huggingface dataset:
Gambaran Methicillin Resistant Staphylococcus aureus (MRSA) Pada Petugas Kesehatan RSUD Wangaya Kota Denpasar

Background. Methicillin Resistant Staphylococcus aureus (MRSA) is a Staphylococcus aureus bacterium that is immune to penicillin isoxazoyl antibiotics such as methicillin, oxacillin and flucloxacillin. Staphylococcus aureus is one of the normal flora of the anterior nares and skin, which can be pathogenic when the patient's immune condition is deteriorating.Purpose. to determine the percentage of Methycillin Resistant Staphylococcus aureus (MRSA) in health staff at Wangaya District Hospital in Denpasar.Method. type of research is descriptive research design with Cross Sectional. The results of nasal swabs were implanted on MSA media and then catalase and coagulase tests were performed. The methicillin resistance test is carried out if after a positive catalase test and a positive coagulase test. If found MRSA will be continued by sensitivity test with antibiotics based on CLSI.Results. found five isolates of Staphylococcus aureus (11.36%), one MRSA isolate (2.27%), sensitive antibiotics were Tetracyclin, Cephazolin, Trimethroprim, Amikacin, Ciprofloxacin, Doxycycline, Ofloxacine and Levofloxacine.Conclusion. One MRSA isolate (2.27%) founded from nasal swabs of health staff at Wangaya District Hospital in Denpasar.
The Use of Team Game Tournament Method to Improve the Students' Reading Comprehension

The objective of this research was to know the effectiveness of Teams Games Tournament (TGT) method to improve the students’ reading comprehension at the eighth grades of SMP N 02 Jepara in the academic year 2018/2019 and also to help the English teachers more creative in teaching reading especially on reading comprehension at eighth grades of SMP N 02 Jepara in the academic year 2018/2019. This research was a true experimental design exactly Pretest-Posttest control group Design that involving two groups of classes. One group treated as the experimental class and the other group treated as a controlled class. In the experimental class, the researcher applied the Teams Games Tournament (TGT) and in controlled class, the researcher applied conventional teaching strategy. The population of this research was the eighth grades students of SMP N 02 Jepara which consisted of 320 students. The samples of the research consisted of 64 students which were taken by using simple random sampling, VIII F consisted of 32 students as experimental class and VIII E consisted of 32 students as a controlled class. There were two variables in this research: Teams Games Tournament (TGT) as the independent variable and students’ reading comprehension as the dependent variable. The instrument of this research was the reading comprehension test. In the reading test, the result of the data indicated that there was a significant difference between students’ post-test in both experimental and controlled class. In the experimental class, the total mean score of the post-test was 94.75 was greater than the total mean score in the controlled class was 77.25. From the t-test, the researcher found that the value of the t-test in the post-test was greater than the t-table (8.616 &gt; 2.000). Based on the findings and the discussion of the research, the researcher drew a conclusion that the use of the Teams Games Tournament was effective to improve the students’ reading comprehension in the eighth grades of SMPN 02 Jepara.
Aplikasi Informasi Pelaporan Dan Penemuan Barang Tertinggal Berbasis Android (Studi Kasus: Pt Kereta Commuter Indonesia)

In carrying out the process of reporting and finding items left behind at PT Kereta Commuter Indonesia, the mechanism is still using making a loss letter. When the Passenger wants to know the items left behind are reported to the station clerk, then he must contact the passanger officer and fill in the loss form, then from the passanger service, coordinate via mobile phone to find out the item's position. The method used in the analysis is the PIECES application method, the development method used is the waterfall method but only to the needs analysis and system design while the data collection methods used in this study are literature studies, observations and interviews. The results of this study are android-based applications that can be used by PT Kereta Commuter Indonesia, Passanger Service Officers in reporting and finding leftovers, the value of paper writing into storage in the database.&#x0D;
Supervised Categorical Metric Learning With Schatten <i>p</i>-Norms

Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development of categorical data still needs further exploration. In this article, we propose a method, called CPML for categorical projected metric learning, which tries to efficiently (i.e., less computational time and better prediction accuracy) address the problem of metric learning in categorical data. We make use of the value distance metric to represent our data and propose new distances based on this representation. We then show how to efficiently learn new metrics. We also generalize several previous regularizers through the Schatten p -norm and provide a generalization bound for it that complements the standard generalization bound for metric learning. The experimental results show that our method provides state-of-the-art results while being faster.
The decomposition of an arbitrary 2 × 2 unitary matrix into signed permutation matrices

Birkhoff's theorem tells that any doubly stochastic matrix can be decomposed as a weighted sum of permutation matrices. A similar theorem reveals that any unitary matrix can be decomposed as a weighted sum of complex permutation matrices. Unitary matrices of dimension equal to a power of 2 (say 2w) deserve special attention, as they represent quantum qubit circuits. We investigate which subgroup of the signed permutation matrices suffices to decompose an arbitrary such matrix. It turns out to be a matrix group isomorphic to the extraspecial group E22w+1+ of order 22w+1. An associated projective group of order 22w equally suffices.
Performance Evaluation of Deep Learning Networks on Printed Odia Characters

Deep machine learning includes a series of layers to mimic the working of the human brain for taking a decision.Deep learning networks have shown good results in character recognition in the past.This paper evaluates the performance of different deep learning networks like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) based recurrent neural network and Convolutional LSTM on printed Odia characters.The Odia character database contains more than 24,000 images of printed Odia characters (including simple as well as complex characters) out of which 23,857 nos. of images are chosen for this work.Besides these three, a nested Convolutional neural network model is developed for different categories of printed character image groups which are formed based on their writing style.Here, in this study, the nested model is showing the best results in terms of error rate, accuracy and no. of epochs in comparison to the other three.Different pre-processing steps like binarization, sizenormalization, blurring, interpolation, etc. are involved before passing the images to the deep neural networks to increase the recognition accuracy.
Agro-Financing and Agricultural Output in Nigeria

This study examined the impact of agro-financing on agricultural output in Nigeria. The data for the study was sourced from the Central of Nigeria (CBN) Statistical Bulletin and the World Development Indicators (WDI) of the World Bank for the period between 1990 and 2019. The study applied the Canonical Cointegration approach to achieve its objectives. Findings showed that agro-financing significantly affect the level of agricultural output in Nigeria. The result of the econometric models estimated in this study showed that, on average, a 1% increase in agro-financing may increase agricultural output by between 0.12%. Therefore, the study recommended that more agro-funding at low-interest rates will motivate farmers to invest in high-yield seedlings, farm implements, organic inputs that are capable of positively impacting total yield leading to more output.
TAM Towards Leddite Building Mandors In The Use Of E-Filing

This study aims to identify perceptions of usefulness, convenience and attitude towards behavior in the use of e-filling. Data from this study were obtain through quesstionnaires distributed to the community in the category of technological stuttering and had produced 50 respondents consisting of construction foremen in PT. Hutama Karya (Persero) Surabaya Brach. This study had a result that found if the variable perceeived usefullness and perceived easse of being has a possitive and siignificant effecct onthe use of, e-filling, but the attitude towards the most behavior did not affect the use of e-filling. This study hal limitation is that it only uses 1 company as a research sample, so it is less able to generalize these findings.
Legal regulation of the European works in European Union and Republic of Serbia

Rapid development of technology has brought a need to digitalize copyright works. This way knowledge, stored in these works, can be shared with every internet user. Problem is, that for many of these works it is not possible to find right owner and so, important knowledge remains hidden from the eyes of society. The need to regulate digitization in cases where the consent of the right holder is missing (including orphan works where the holder is inaccessible) has also been recognized in the European Union, first through a project called Europeana. The main objective of this project is to disseminate knowledge by creating digital libraries that would contain all the copyrighted works that users can access from anywhere and without any time limit. This problem is partially resolved in European Union in 2012 when the Directive 2012/28/EU on certain permitted uses of orphan works was introduced. This directive gives instructions for all members of the EU to recognize and limit the use of orphan works in their countries accordingly to the rules given in the Directive. The Republic of Serbia, as an EU membership candidate, needs to harmonize its legislation system with rules provided in EU law and include the definition of orphan works and usage of orphan works into its copyright rules.
Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.
Analisa Komparasi Perangkat Speech Recognizing dan Potensinya dalam Membantu Proses Pembelajaan Difabel Rungu Guna Terciptanya Kampus Inklusif Di Era 4.0

The lack of facilities and infrastructures for students with special needs will hamper the learning process. In fact, this has been explained in the definition of inclusive education which is to accommodate students with special needs to comprehend the material in the class. The fact is that there are only public facilities such as the guiding block for the blind, and some interpreters in the class to explain the material. This situation is exacerbated by the limitations of an interpreter to explain scientific terms. Therefore, we need a technology that can help deaf people to comprehend the material in the classroom directly. The technology is speech recognizing device. This device enables to process input signals in the form of sound and is converted into text. This technology enables deaf people to comprehend material in the classroom without an interpreter. The research method is inferential statistics and observations. Observations were made by measuring 26 respondents speaking speed to obtain data using 3 different applications namely Speech Notes, Voice Notebooks, Speech to Text. Afterthat, the words obtained in the application are compared with the actual text to see the accuracy of each application. By using inferential statistics, the correlation test values obtained in the application of Voice Notebooks, Speech Notes, Speech to Text are 0.386,0.351, and 0.152, respectively. By using 5% significance level, we found that the most accurate speech recognizing application is Voice Notebook. Due to Voice Notebook application, we can support the special students in learning process in the class.
An improved boundary-aware face alignment using stacked dense U-Nets

Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked dense U-Nets. The proposed method consists of two stages: a boundary heatmap estimation stage to learn the facial boundary lines and a facial landmark localization stage to predict the final face alignment result. With the constraint of boundary lines, facial landmarks are unified as a whole facial shape. Hence, the unseen landmarks in a shape with occlusions can be better estimated by message passing with other landmarks. By introducing the stacked dense U-Nets for feature extraction, the capacity of the model is improved. Experiments and comparisons on public datasets show that the proposed method obtains better performance than the baselines, especially for facial images with large pose variation, shape variation, and occlusions.
Testing Procedure for Item Response Probabilities of 2Class Latent Model

This paper presents Hotelling T2 as a procedure for the testing of significance difference between the item response probabilities (ωij′s) of classes in a Latent Class Model (LCM). Parametric bootstrap technique is used in order to generate samples for ωij′s. These samples are based on the estimated parameters of 2-class latent model. The estimation of parameters in either situation is done using the Expectation Maximization (EM) algorithm through Maximum likelihood method. The hypothesis under consideration is whether the response probabilities (ωij′s) are equal against each item in both the classes. { H0 : ωi1 = ωi2. against H1 : =ωi1 ≠ ωi2}. If the test exhibits significant difference between response probabilities in both classes, it will be a clear indication of a presence of latent variable. We consider both training and testing data sets to develop the test. In order to apply Hotelling T2 test the basic assumptions of normality and homogeneity of variance are also checked. Chi-square goodness of fit test is used for assessing normal distribution to be good fitted on the hypothesized (bootstrap samples) based on 2-class latent model parameters for each data and Bartlett test to check heterogeneity of variances in ωij′s. Moreover, our procedure produces a minimum standard error of estimates as compared to those obtained through the package in R.Gui environment
Perbedaan Hasil Belajar Siswa Menggunakan Bimbingan Belajar Online

This study aims to analyze whether there are differences in learning outcomes of students who take online tutoring and students who do not take tutoring. The population of this study was the students of class X in Padang 3 high school who took Ruangguru online tutoring and the sample technique used was proportional random sampling using the Slovin formula so that the number of samples obtained was 58 students. The hypothesis of this study was tested using One Way Aonva analysis. The results showed that there was no difference in the average learning outcomes between groups of students who took Ruangguru online tutoring and groups of students who did not take tutoring.
Digital humans on the big screen

Motion pictures are using new techniques in computer-generated imagery to create feature-length performances by convincingly "de-aged" actors.
ELECTRONIC EDUCATIONAL RESOURCES AS A MEANS OF FORMATION OF DIGITAL COMPETENCE OF PRIMARY SCHOOL STUDENTS IN THE PROCESS OF ONLINE AND OFFLINE EDUCATION

Today the world is in the process of rapid informatization and digitalization of society in all its spheres. The way and pace of life of a modern man is not similar to the life of previous generations. The spread of the coronavirus disease, known as COVID-19, has necessitated acceptance and adaptation to new working and learning conditions for educators from different countries. Educational institutions around the world are responding to numerous prohibitions and quarantine rules with the transition to distance learning. The crisis has already caused an online boom for education and forced teachers to be preparedto deal with this atypical situation. The most powerful source today is digital technology, or as they call it - digital learning tools, which implies competent use of the latest gadgets. Of course, global changes in public life cannot take place without changes in the educational space, so our education system now follows the experience of advanced European countries and implements many programs for the formation and development of digital literacy, including at the educational level. Definitely, Ukrainian education system follows the experience of advanced New Ukrainian School provides for the widespread use of information and communication technologies by each teacher in their own professional and pedagogical activities, which is an essential way to update and informatize the education content in different education institutions of our country. This process is impossible without the systematic use of electronic educational resources (EER) during the educational process, because they definitely create a modernized content of educational space and provide equal access to educational online materials to each participant in the educational process regardless of location, age or other aspects.
Foreground-aware Dense Depth Estimation for 360 Images

With 360 imaging devices becoming widely accessible, omnidirectional content has gained popularity in multiple fields.The ability to estimate depth from a single omnidirectional image can benefit applications such as robotics navigation and virtual reality.However, existing depth estimation approaches produce sub-optimal results on real-world omnidirectional images with dynamic foreground objects.On the one hand, capture-based methods cannot obtain the foreground due to the limitations of the scanning and stitching schemes.On the other hand, it is challenging for synthesis-based methods to generate highly-realistic virtual foreground objects that are comparable to the real-world ones.In this paper, we propose to augment datasets with realistic foreground objects using an image-based approach, which produces a foreground-aware photorealistic dataset for machine learning algorithms.By exploiting a novel scale-invariant RGB-D correspondence in the spherical domain, we repurpose abundant non-omnidirectional datasets to include realistic foreground objects with correct distortions.We further propose a novel auxiliary deep neural network to estimate both the depth of the omnidirectional images and the mask of the foreground objects, where the two tasks facilitate each other.A new local depth loss considers small regions of interests and ensures that their depth estimations are not smoothed out during the global gradient's optimization.We demonstrate the system using human as the foreground due to its complexity and contextual importance, while the framework can be generalized to any other foreground objects.Experimental results demonstrate more consistent global estimations and more accurate local estimations compared with state-of-the-arts.
EFEKTIVITAS PENGELOLAAN DANA ZIS PADA BIDANG PENDIDIKAN DI LAZISMU KOTA SURABAYA

This study aims to determine the effectiveness of ZIS funds in the field of education at LAZISMU Surabaya City. The research method used in this study was descriptive qualitative with a research strategy using case studies. Data collection carried out in this study is by observation, interviews, and documentation. Data analysis techniques were by reducing data, presenting data, and drawing conclusions. The results of this study indicate that ZIS funds managed by LAZISMU are effective, including input standards, process standards and output standards. To help the educational sector, LAZISMU needs to increase educational scholarship funds.Keywords: effectiveness, education fund, ZIS, LAZISMU
Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.
Learning Capacity in Simulated Virtual Neurological Procedures

In recent years, the use of Virtual Reality (VR) has skyrocketed in many fields.From videogames to education, the popularity of this technology is well known also thanks to its affordability and the growing number of available content.An immersive approach and the correct use of gamification have been proven to be valuable tools for learning in different fields.There are many examples of the adoption of VR application also in the healthcare setting not only for learning purposes but also for patients' rehabilitation.This paper is intended to list some existing works and then present our project for an immersive serious game with the aim of enhancing the training of healthcare workers for the assessment of stroke patients.