Reviewerly/exHarmony huggingface dataset:
KINERJA PENGISIAN DOKUMEN REKAM MEDIS DI PUSKESMAS MADAPANGGA BIMA NTB

The percentage of incomplete filling of medical record documents at the Madapangga Health Center shows that the Minimum Service Standards in the Hospital are not yet 100%.Incomplete data has shown that the completeness of filling out medical record documents is still not up to the specified standard.The incompleteness of filling out the medical record document may be caused by the performance factor of the officer in filling out the medical record document for inpatients.The purpose of this study was to analyze the performance factors in filling out medical record documents for inpatients at the Madapangga Public Health Center.This study uses qualitative research that aims to identify and analyze the performance factors of filling out inpatient medical record documents at the Madapangga Health Center, which will be linked to performance theory with personal factors, leadership factors, team factors, system factors, and situational factors, and using the method USG (Urgency, Seriousness, Growth) to determine the main factors of the 5 factors that affect the performance, as well as efforts to fix problems using brainstorming.The results of this study obtained that the priority causes of the problem of incomplete filling of medical record documents for inpatients at the Madapangga Health Center were the lack of awareness of each individual regarding filling out medical record documents, lack of evaluation and monitoring, lack of socialization, lack of understanding related to SOPs for filling out medical record documents due to SOPs for filling out medical records.There is no medical record document yet, so the Puskesmas Madapangga asked the researchers to make an SOP for filling in inpatient record documents.As a suggestion, it is carried out to raise commitments for the completeness of filling out medical record documents, conducting socialization, evaluation and monitoring on a regular basis, as well as making SOPs for filling out medical record documents.
Power-efficient Neural Network Scheduling

The powerful deep neural networks (DNNs) have been propelling the development of efficient computer vision technologies for mobile systems such as phones and drones. To enable power-efficient image processing on resource-constrained devices, many studies have been dedicated to the field of low-power DNNs from different layers of the systems. Amongst the deep stack of low-power DNN systems, task scheduling also plays an essential role as the interfacing middleware between the algorithms and the underlying hardware. Especially when heterogeneous SoCs have been widely adopted in edge and mobile scenarios as the hardware solution, an efficient DNN task scheduler is needed to reduce the implementation overhead of DNN-based task and extract the most power from the SoC platform. This chapter will firstly exemplify DNN scheduling with the image recognition solution of LPIRC-2016 and introduce how to efficiently schedule a DNN-based visual processing task onto a typical heterogeneous SoC composed of general-purpose and specialized cores. After the elaborate task-level scheduling strategy, we will discuss the fine-grained DNN-wise scheduling policy on specialized DNN cores and show the effectiveness of memory-oriented DNN-layer scheduling. Last, since model quantization is an indispensable step to map a large-size neural network model onto the resource-thrifty mobile SoCs, we will discuss the implication of DNN quantization on the heterogeneous SoCs integrated with both integer and float-point cores, and then introduce the scheduler-friendly DNN quantizer for pure-integer hardware. Although most prior works on low-power DNNs focused their attention on efficient network and hardware architectures, it is shown that the scheduler-level optimization technology will also be critical to the energy-efficiency of the system, particularly when the algorithmic implementation is fixed and off-the-shelf hardware devices are adopted. Take-aways Demonstrates the rank-1 solution of LPIRC2016 as a case study to introduce the basic coarse-grained scheduling techniques for DNN-based applications. Presents the memory-efficient fine-grained neural network scheduler on DNN processors. Introduces the scheduler-friendly quantization technique to reduce the overhead of neural network implementation on embedded SoCs.
Using an ensemble of FAIR assessment approaches to inform the design of future FAIRness testing: a case study evaluating World Data Center for Climate (WDCC)-preserved (meta)data

From a research data repositories' perspective, offering data management services in-line with the FAIR principles is becoming more and more of a selling point to compete on the market.In order to do so, the services offered must be evaluated and credited following transparent and credible procedures.Several FAIRness evaluation methods are openly available for being applied to archived (meta)data.However, there exists no standardized and globally accepted FAIRness testing procedure to date.Here, we apply an ensemble of 5 FAIRness evaluation approaches to selected datasets archived in the WDCC.The selection represents the majority of WDCC-archived datasets (by volume) and reflects the entire spectrum of data curation levels.Two tests are purely automatic, two are purely manual and one test applies a hybrid method (manual and automatic combined) for evaluation.The results of our evaluation show a mean FAIR score of 0.67 of 1. Manual approaches show higher scores than automated ones.The hybrid approach shows the highest score.Computed statistics show agreement between the tests at the data collection
IMPACT OF COLLAGE MAKING UPON THE DEVELOPMENT OF CREATIVE SKILLS OF CHILDREN AT THE AGE OF 7-8

The focus of the paper is on the collage, which as a creative mix of various forms and structures, finds broad application in modern art, especially when it comes to fields such as fine and applied arts, advertising, modern design etc. The origin of the word comes from French, coler (glue), which is also the way it has been translated in Bulgarian.Collage is normally an authentic piece of art which combines different materials glued together, for instance paper, press-cuts from newspapers and/or magazines, photos, cloth, sometimes even wood, etc. The collage not only offers a new piece of art, it also shows its author’s attitude to reality, being its specific reflection. Therefore, nowadays more and more artists, writing for children and not only, would turn to collage as a form of art.This gives us a strong ground to write about the collage and try to analyze its influence on children’s artistic skills. The paper focuses on that issue, using our in-depth research work with children at the age of 7-8, who were asked by using collage to do illustrations of a book they were not familiar with, namely Barba Family by Annette Tison and Talus Taylor.The main goal of the research has been to analyze, test and offer a comparative perspective of the way collage techniques implicate the development of creative skills of children at the above-mentioned age. The methods used include observation, comparative analysis and experimental work. The results obtained have given us a strong ground to formulate various conclusions and offer recommendations in regard to the improvement of the creative artistic skills of the children included in the researched target group. Our experiments with children using collage techniques undoubtedly show the positive effects of collage on children’s creativity, as well as on their overall attitude toward both art and reality. Our research has also shown that usage of collage techniques affects positively teachers’ work, and especially that of preschool and primary teachers in arts.
Optimization Techniques to Solve Travelling Salesman Problem Using Machine Learning Algorithms

Abstract: Travelling Salesmen problem is a very popular problem in the world of computer programming. It deals with the optimization of algorithms and an ever changing scenario as it gets more and more complex as the number of variables goes on increasing. The solutions which exist for this problem are optimal for a small and definite number of cases. One cannot take into consideration of the various factors which are included when this specific problem is tried to be solved for the real world where things change continuously. There is a need to adapt to these changes and find optimized solutions as the application goes on. The ability to adapt to any kind of data, whether static or ever-changing, understand and solve it is a quality that is shown by Machine Learning algorithms. As advances in Machine Learning take place, there has been quite a good amount of research for how to solve NP-hard problems using Machine Learning. This reportis a survey to understand what types of machine algorithms can be used to solve with TSP. Different types of approaches like Ant Colony Optimization and Q-learning are explored and compared. Ant Colony Optimization uses the concept of ants following pheromone levels which lets them know where the most amount of food is. This is widely used for TSP problems where the path is with the most pheromone is chosen. Q-Learning is supposed to use the concept of awarding an agent when taking the right action for a state it is in and compounding those specific rewards. This is very much based on the exploiting concept where the agent keeps on learning onits own to maximize its own reward. This can be used for TSP where an agentwill be rewarded for having a short path and will be rewarded more if the path chosen is the shortest. Keywords: LINEAR REGRESSION, LASSO REGRESSION, RIDGE REGRESSION, DECISION TREE REGRESSOR, MACHINE LEARNING, HYPERPARAMETER TUNING, DATA ANALYSIS
On the use of Virtual Negative Resistance in Physical Selective Electronic Circuits

The objective of the work is to present some aspects related to the use of negative resistances in selective electronic circuits, i.e., based on capacitors and inductors (RLC circuits). The results of the computer-based experiments, with ideal and real models of the electronic components, are presented and compared, also from stability point of view. Next, the results of the physical experiments with real operational amplifiers are discussed. All these aspects promote and prepare a better understanding and wider use of the electronic circuits with negative resistances in various applications, as e.g., pass-band filters, adjustable active loads, and harmonic oscillators.
UX Research and Sonic Interaction: Towards Human-Centric and Intuitive Sound Interaction Design in the Context of Autonomous Driving

OneSonic interaction design of the major challenges of user experienceUser experience (UX) design in autonomous drivingAutonomous driving (AD) is to investigate the emerging expectations and needs of future passengers. Working with foresight into future interactions requires interdisciplinary collaboration that enables new modes of working. In our everyday work and throughout the sound interaction design process, we are often challenged by the lack of exploratory methodologies for involving human insights early on in the sound interaction design process. This chapter draws on the interviews with experts in sound design and UX domains, the research project, Sound Interaction for Intelligent Cars (SIIC), as well as daily product development work at the Volvo Car Corporation (VCC) to address this gap. We characterise what we call the classic approach to sound interaction design and outline methodologies used at different stages of the design thinkingDesign thinking process for generating and translating UX insights into sonic interaction solutions. By this, we broaden the way of practicing participatory sound UX for AD development and respond to the challenge by incorporating existing exploratory design research methodologies, such as sensory ethnographySensory ethnography, soundwalk, co-creation, ideation, and rapid prototyping.
Implementation of ICT and Digital Technologies Within Higher Education

We are facing many challenges due to COVID-19 crisis in many sectors, higher education is no exception.Although higher education systems are mostly traditionally oriented offering traditional study programmes using traditional study methods and techniques, with implementation of ICT and digital technologies, they can effectively answer many of the challenges related to COVID-19 crisis.Aim of this paper is to show how ICT and digital technologies could help in higher education system so that the educational process could continue despite all restrictions and challenges of COVID-19 crisis.Main focus will be on different aspects of how to use ICT and digital technologies within higher education systems: within the learning process, application of new students for study programmes, communication with teachers, communication and services with the student office, communication and services with other offices related to student activities and other activities related to student life.
Performance analysis of lightweight Internet of things devices on blockchain networks

Potential integration or cooperation of the Internet of things (IoT) systems and the blockchain technology is nowadays attracting remarkable interest from the researchers. These inter-operating systems often have to rely on lowcost, low-power, and robust IoT devices that can communicate with the blockchain network through smart contracts. In this work, we designed and ran a benchmark study for ESP32-based lightweight IoT devices interacting within the Quorum blockchain. A software library was built for ESP32 devices to enable elliptic-curve digital signing, Keccak-256 hashing, decoding, encoding, and secure private key generation capabilities, which all are the basic functional requirements for running a blockchain client. The running times and power consumption values of these essential operations were analyzed and discussed in detail. We also deployed two smart contract functions on the Quorum network and analyzed the performance of the IoT device while interacting via these methods. Further optimizations were proposed and implemented to speed up the processes and save energy, which resulted in a 2.5x increase in the transaction posting speeds and a 2/3 decrease in the energy costs. Another performance comparison between an ESP32 and a PC client was also provided with.
ANALYSIS OF DIGITAL LITERACY COMPETENCIES OF ECONOMICS TEACHERS IN ONLINE LEARNING DURING THE COVID 19 PANDEMIC

This study aims to describe the digital literacy of economics teachers in online learning as an effect of the COVID-19 pandemic. The approach used in this research is descriptive quantitative approach with explanatory survey method. Simple random sampling techniques was used for collecting the data. From the total population of 135 economics teachers, the researcher took a sample of 56 economics teachers, where this sample was taken from 28 public schools consisting senior high schools and Islamic high school in Tasikmalaya Regency. Respondents were given statements totaling 26 items with two choices, namely Yes and No, then the collected data was given a score based on the Guttman scale. The questionnaires that have been distributed are tabulated for easy calculation. After the data was tabulated, the results were described according to the conditions in the field. The results of this study indicate that the digital literacy of economics teachers in online learning during the Covid 19 pandemic is relatively high, namely 55.30%, however, if viewed from the four aspects of digital literacy skills for the guidance aspect of hypertext, it is still low. Therefore, evaluation is still needed to improve teachers' understanding of hypertext, so that teachers will be more effective in accessing existing information to prepare for online learning.
Public Sentiment Analysis of Online Transportation in Indonesia through Social Media Using Google Machine Learning

Public opinion is important to agencies or parties in particular fields, as it may indicate a tendency of public's view towards something (such as an object or process). One of them is in the transportation sector. Transportation has become a necessity for the community, many things more effective and efficient online, so that online transportation becomes important for society. The proliferation of online transportation, caused citizens to express opinions through social media. It is important to know the level of service of online transportation considering the large number of users, so that it can be used as a basis for improvement. One of the methods public opinion in social media is by sentiment analysis. The study used the help of Google Machine Learning for the sentiment analysis process that can produce 82,6% of accuracy number, 82,2% of precision, 83,3% of recall with the most sentiment result indicate to public opinion falls into the negative sentiment category for Gojek companies in media social of Twitter.
EDUCATIONAL COMPETENCIES OF SPECIALISTS WORKING IN YOUTH ORGANIZATIONS IN RURAL AREAS

Modern problems of work with youth in rural areas determine the necessity of identification of educational competencies of specialists working in youth organizations, which determines the directions in life of young people. The purpose of the article is to study the most important educational competencies of specialists working in youth organizations in rural areas, based on the analysis of the impact of educational competencies of specialists on the directions in life for youth. Educational competencies of youth work include both general ethical competencies and specific professional competencies related to youth work. The study has been conducted in 2016 and 2021 to compare changes in educational competencies. The structural equation modeling to solve the problem of identifying the relationships between ethical and professional competencies, and the directions in life for youth, and sociological survey to identify the specific area of interest of young people living in rural areas was used.
Organizing Code

Programs are rewritten and modified far more often than they are ever created. That means most of the time developers must change and modify existing code either written by someone else or written by you sometime in the past. Since you may be writing code that you or someone else will eventually modify in the future, you need to make sure you organize your code to make it easy to understand.
Arterial hypertension and cerebrovascular accidents. epidemiological profile of patients attended between 2013 and 2019 in Joinville, Santa Catarina, Brazil.

Introduction: Cerebrovascular accident (CVA) is the second cause of death in the world, and arterial hypertension (AH) is the main risk factor. Objetives: To evaluate the prevalence of AH and the epidemiological profile of patients who suffered CVA, first and recurrent events, registered at JOINVASC, between 2013 and 2019. Methodology: An observational, descriptive, retrospective study, analyzing demographic data and risk factors, in patients that suffered CVA, first or recurrent event, was done. Results: In the study period, 6057 CVA events were registered, 4402(72.6%) patients were classified as hypertensive, (122 were excluded due to incomplete data), 4387 registers were analyzed. In this group, 2149 (51.2%) were male, mean age was 68.4±14.7(18 a 102 years), BMI was 27.5±5.1(14 a 59), 4330(39.9%) with Diabetes, 2559(58.8%) were smokers or ex-smokers, 3174(7.42%) were sedentary, and 504(11.5%) had controlled AH. In the group with recurrent event (1392-31.7%) (52.3%, p&lt;0.001) were females, mean age was (69.4± 11.5) (p&lt;0.002); BMI was lower (p=0.02). There was more cardiopathy (47.6%) (p&lt;0,001) ,smokers or ex-smokers (66.2)(p&lt;.005), diabetes (46.7%) (p&lt;0.001) and deaths (32.3%)(p&lt;0.001), less physical activity (22.0%)(p&lt;0.001), use of antihypertensive drugs was greater (1.52±0,93)(p&lt;0.001),. Logistic regression showed that female sex, BMI, DM, cardiopathy, physical activity, and more anti- hypertension drugs, were significant predictors for recurrence of neurologic event. Discussion: Despite advances in primary health care and prevention campaigns, the prevalence of AH in patients with CVA is high, and still has low levels of control, even in patients with recurrent CVA.
A Comparison between Seven Heuristic Methods to Estimate the Number of Hidden Layer Neurons in a Multi Layer Perceptron Neural Network

Multilayer Perceptron Neural Network (MLPNNs) constructs of input, at least one hidden and output layer. Number of the neurons in the hidden layer affects the NNs performance. It also consider difficult task to overcome. This research, aims to exanimate the performance of seven heuristic methods that have been used to estimate the neurons numbers in the hidden layer. The effectiveness of these methods was verified using a six of benchmark datasets. The number of hidden layer neurons that selected by each heuristic method for every data set was used to train the MLP. The results demonstrate that the number of hidden neurons selected by each method provides different accuracy and stability compared with other methods. The number of neurons selected by Hush method for ine data set was 26 neurons. It’s achieved the best accuracy with 99.90%and lowest accuracy achieved by Sheela method with 67.51% using 4 neurons. Using 22 neurons with 97.97% accuracy Ke, J method received the best result for Ionosphere data set. While the lowest accuracy was 96.95% with 5 neurons achieved by Kayama method.For Iris data set with 8 neurons achieved 97.19 as best accuracy achieved by Hush method. For the same data set the lowest results were 92.33 % using 3 neurons obtained by using Kayama method. For WBC data set 96.40% the best accuracy achieved using Sheela and Kaastra methods using 4 and 7neurons, while Kanellopoulos method achieved the lowest accuracy 94.18% with 7neurons. For Glass dataset, 87.15% was the best obtained accuracy using 18 neurons Hush method and using Wang method 82.27 % with 6 neurons was the lowest accuracy. Finally for PID 75.31% accuracy achieved by Kayama method with 3 neurons, where Kanellopoulos method obtained 72.17% through using 24 neurons.
Disruptive Neuro - Heretical in Heterodox Decision Management

Objective of this paper is to bring out inventive reflection upon ‘busitagion’ and managing change through visualizing goals of change. In terms of tactic, a multi - level heterodoxian scrutiny is applied that allows investigations of intuition - based ‘disruptive mental’ approximations’ making to conceive ‘busitagion’ management from a principle - based perspective while representing interdisciplinary turf of ‘Homo - Psychoeconomicus’ sophistications. An attempt is made to address the research problem based on empirical study conducted that provide synthesis of examining new heterodoxian hematological psychological mechanisms and heterodoxian strategies underlying theories and methodological approaches within the loosely coupled community of organizational scholars by taking account of fact that micro phenomena are embedded in macro contexts, and macro phenomena that often emerge through interaction and dynamics of lower-level elements.
Impact of Financial Technology on Accounting and Auditing

With a dynamic industry environment, it is now a mandate for the organisations to invest time, money and efforts in adapting to the future way of doing things, at present. A cloud system is part of the basic infrastructure now. When the entire business model changes so does change the way of accounting and auditing. Robotics is being used to do the accounting free of manual intervention whereas the machine learning and artificial intelligence is helping identify patterns and generates the exception reports which leaves the auditor with specific grey areas.