Reviewerly/exHarmony huggingface dataset:
Integration Information System Program "Gancang Aron" In Banyuwangi Hospital

The government seeks to improve public services through the emergence of innovative ideas, one of which is with the "Gancang Aron" program at a hospital in Banyuwangi. This study aims to provide a particular system design integrated with the primary program system to minimize errors in the pharmacy department. This research uses a descriptive qualitative approach with case studies—data obtained through observations, interviews, and documentation. The analysis technique used is the input-process-output-based information system development. This system's development will provide optimization of services, especially in the medicine section, to avoid errors that will impact reports and decision-making. This research implies that the system design results can serve as a basis or model for the development of information systems in various organizations and a guide in making policies and decision making.
Modeling and Controlling Alternate Circuits of the Educational Robot R5150 using Matlab and an Arduino Chip

In this article, we worked on the description of the mathematical model of the serial robotic manipulator with five degrees of freedom within the Matlab environment, where the educational laboratory model ROBOT 5150, which is the manipulator of the LAB VOLT family, which has five degrees of freedom and an effective end (pickup) was used as a case study through the kinetic study (study of forwarding and reverse movement) of this robot and its characterization within the Matlab/Simulink environment (modeling it), And alternative circuits were modeled and designed to control this arm using the Arduino mega 2560 chip and Matlab/Simulink software by linking the virtual environment in Matlab with the practical circuits. We have changed the existing controller in the design by the manufacturer and using stepper motors and servo motors, intending to make this article a reference so that students can build and develop programs and programming interfaces without restrictions imposed on drivers and maintenance programs and also use the designed program as an educational tool To gain a good experience in the field of robotic arm control and an understanding of how to program these maneuvers.
A Study on the Intuitive World and Formative Expression by Line

In recent days, as the art creation patterns are mechanized in line with the development in online media and digital technologies, it is more critically required the expression of line that becomes a base in the painting.The expression of lines is a part of the art creation expression method, and it handles the recognition and expression processes of the difference by separating them from intuition and mimesis-like.In addition, by visualizing the intuitive world and the appearance of the objects, it is artisticized as a representation of the spiritual world in a formative language.In this way, the expression by line explains that the formative expression itself can be a work of art, beyond simply expressing the object intuitively and being used as a tool for expressing the appearance.In this context, this study is now dividing the perceptional concept of line, which is a classical composition method in painting maintained even in modern art that is being digitized, into the East and the West.It confirms the shape expression according to intuition and mimesis as works of art and reveals that it continuously plays a role as a source of creation methods within modern art where the expression of lines is digitized.
Cloud based evaluation of databases for stock market data

About fifty years ago, the world's first fully automated system for trading securities was introduced by Instinet in the US. Since then the world of trading has been revolutionised by the introduction of electronic markets and automatic order execution. Nowadays, financial institutions exploit the associated flow of daily data using more and more advanced analytics to gain valuable insight on the markets and inform their investment decisions. In particular, time series of Open High Low Close prices and Volume data are of special interest as they allow identifying trading patterns useful for forecasting both stock prices and volumes. Traditionally, relational databases have been used to store this data; however, the ever-growing volume of this data, the adoption of the hybrid cloud model, and the availability of novel non-relational databases which claim to be more scalable and fault tolerant raise the question whether relational databases are still the most appropriate. In this study, we define a set of criteria to evaluate performance of a variety of databases on a hybrid cloud environment. There, we conduct experiments using standard and custom workloads. Results show that migration to a MongoDB database would be most beneficial in terms of cost, storage space, and throughput. In addition, organisations wishing to take advantage of autoscaling and the maintenance power of the cloud should opt for a cloud native solution.
Fast Fourier Transformation Based Evaluation of Microlensing Magnification with Extended Source

Abstract The extended source effect on microlensing magnification is nonnegligible and must be taken into account for an analysis of microlensing. However, the evaluation of the extended source magnification is numerically expensive because it includes the two-dimensional integral over the source profile. Various studies have developed methods to reduce this integral down to the one-dimensional-integral- or integral-free form, which adopt some approximations or depend on the exact form of the source profile, e.g., a disk or linear/quadratic limb-darkening profile. In this paper, we develop a new method to evaluate the extended source magnification based on fast Fourier transformation (FFT), which does not adopt any approximations and is applicable to any source profiles. Our implementation of the FFT based method enables the fast evaluation of the extended source magnification as fast as ∼1 ms (CPU time on a laptop) and guarantees an accuracy better than 0.3%. The FFT based method can be used for the template fitting to a huge data set of light curves from the existing and upcoming surveys.
Trajectory Planning of Tilt-Rotor UAV Based on Hybrid Algorithm

Tilt-rotor UAVs are exceptionally suitable for near-ground unmanned transportation and other fields because of their helicopter mode, transition mode and fixed-wing mode. However, the tilt-rotor transition on trajectory planning presents a challenge, as performance constraints of different modes must be considered and a trajectory planning algorithm is required to optimize a collision-free and short trajectory with low fuel consumption and small cost. To address these challenges, this paper adds performance constraints of the tilt-rotor UAV to the hybrid algorithm by combining the A* algorithm and the artificial potential field method; meanwhile, the rationality of trajectory planning is improved by setting the tilt-rotor transition region separately in the global. Simulation results show that a reasonable trajectory can be planned by setting the tilt-transition region by itself and using a hybrid algorithm.
Automatic Recognition for IoT Supervision Images Based on Modal Decomposition

The automatic recognition for Internet of Things (IoT) supervision images is a prerequisite for the detection of abnormalities in monitoring images. This technology is a developmental trend in video surveillance. Various video image detection and recognition methods have certain weaknesses, such as poor generalization ability and poor anti-interference ability. In response, this paper conducts a study on automatic recognition for IoT supervision images based on modal decomposition. The paper presents an overall framework of the IoT supervision system. For the problems of poor real-time performance and few samples that commonly exist in video stream target recognition, the paper proposes a dynamic modal decomposition-based feature extraction algorithm for IoT supervision video stream to build a suitable platform for IoT supervision image foreground segmentation. The paper selects a dictionary with rich elements and exchanges a high computation time for minimizing the reconstruction error generated by applying the dynamic modal decomposition method. Experimental results validate the effectiveness of the proposed algorithm.
Exploring the Business Model Innovation of Blockchain Technology

Blockchain is a technological innovation that performs a different paradigm (decentralized protocol) to change the way organizations or societies interact with others. Blockchain adoption evolves into different ecosystems (network of actors adopting BC) along with its development, namely public and private ecosystems. Although previous research has examined the role of BC on the business model, there is little explanation for how BC applies business model innovation (BMI) in different ecosystems. Using a multiple-case study approach, this study seeks to explain the innovation of the business model in different BC ecosystems. The study finds different formulas of BMI among the ecosystems. The potential BMI in the private/consortium BC emphasizes improving the existing business values. In the public BC, BMI potentially disrupts the current business model. The study also proposes a new theoretical model to identify the potential of BMI within an ecosystem. This study contributes to the technology management literature, particularly BC and BMI. Furthermore, the practical implications of this study are presented in this investigation.
Modelling Using Deep Learning

Machine learning, and in particular, deep learning has emerged as an important tool for advancing science, in addition to its broad based impact on the world. This talk describes three research efforts that illustrate how deep learning can complement modeling and simulation to pursue scientific discoveries and to tackle societal problems. We begin by describing a flood forecasting initiative that has already led to hundreds of thousands of alerts being sent to people in India. It utilizes a new hydrologic model that has been built using an LSTM (long short-term memory) architecture and a physics based inundation model whose effectiveness has been enhanced using machine learning methods. We also describe how self supervised learning is being applied to study several interesting aspects of the organization of the human brain. The generated embeddings can be used to rapidly annotate new structures and develop new ways of clustering and categorizing brain structures based on purely data-driven criteria. Finally, we present a deep learning based modeling of human behavior in a specific game-based setting, which has very interesting implications if we are able to generalize that approach to broader settings.
Pengaruh Tingkat Intensitas Komunikasi Orangtua terhadap Prestasi Belajar Siswa

The implementation of education is a shared responsibility, the government, the community, and parents. However, the implementation has not yet been agreed upon by the interested parties. Parents fully trust the school with the hope that their child will get a good education. Schools and families contribute to the achievement of the quality of education, and the quality of learning depends on the condition of the students' families. Learning achievement is the result of an educational system so that the level of success is determined and influenced by elements of the system, such as input input, input input, and environmental input. The factors that influence learning achievement are external and internal factors. However, the focus of the study in this research is external factors in the form of social and non-social factors. Social factors include the family environment including how to educate, home atmosphere, community, playmates, teachers and staff at school. This study aims to determine the effect of the intensity level of parent-student communication on the level of student achievement at MTs Al-Bazari Parungkuda Sukabumi. Sampling was carried out in a systematic random sampling so that a sample of 60 people was obtained from a population of 242 people. Collecting data by observation, questionnaires, and documentation. The data obtained were analyzed by descriptive statistics, quantitative and inferential statistical analysis. The results showed that the intensity level of parent-student communication had a positive and significant effect on the level of student achievement with the results of the regression equation obtained were = 5.429 + 0.0334x and the value of Fh = 5.410 which had a value of 0.024 which was smaller than = 0 ,05. This means that the regression model obtained is significant and can be used to estimate the value of y if x is known, and the value of the determinant R2 = 0.085 which means that there is an 8.5% variation in the level of student learning achievement influenced by the intensity level of parent-student communication.
Non-Gaussian state design with the quantum frequency processor

We develop an approach for engineering non-Gaussian photonic states in discrete frequency bins. Combining the quantum frequency processor and photon-number-resolving detection, simulated examples demonstrate the potential for producing high-fidelity cat states with reasonable resource requirements.
The Effects of Motion Sickness on Balance and Gait

Background:The term "motion sickness" (also called "kinetosis") describes a set of symptoms that occur in association with motion of a person or his or her surroundings, triggering a stress reaction that results in autonomic symptoms.The onset is often insidious, with drowsiness/yawning, dizziness, reduced alertness, and symptoms progress through cold sweating, pallor, excessive salivation, and occasionally headache, to nausea and vomiting with incapacitation that can be severe.Motion sickness usually occurs due to a visuo-vestibular conflict.Part of it is psychological as well as the symptoms of motion sickness are triggered in some people by just reading about it. Method:A cross-sectional study was performed in college going students for six months using the MSSQ-short and the Mini BESTest to assess the susceptibility of motion sickness as well as balance and gait respectively. Conclusion:Balance and gait was found to be significantly affected in people with motion sickness.
EVALUASI PENERAPAN PROGRAM IPAL (INSTALASI PENGOLAHAN AIR LIMBAH) KOMUNAL DI KOTA BANDA ACEH

One solution for domestic waste treatment is the operation of a communal IPAL (WWTP, Wastewater Treatment Plant). This is in accordance with SDG's (Sustainable Development Goals), national and regional policies related to sustainable sanitation management. In Banda Aceh, the Sanimas (Community sanitation) program has been and is being implemented in 40 gampongs (villages) which are classified as slum areas. This study aims to determine the results of the effluent processing of the IPAL system (pH, BOD, COD, TSS, ammonia, Total Coliform, Oil/Fat parameters) to see whether the IPAL system that has been built is operating properly. There were five IPALs studied, namely in Gampong Peunayong (Dusun Cendrawasih), Gampong Tibang (Dusun Tgk. Meurah, Dusun Tgk Meulinje, Dusun Tengku Meulagu), and Gampong Panteriek (Dusun Jeumpa). The results of the effluent wastewater test showed that in all WWTPs, the BOD and COD parameters still exceeded the wastewater quality standards, and in one WWTP, the total Coliform parameter was &gt;1,000/100 ml (quality standard &gt;3,000/100 ml).
Application of a New VAE-MF Generative Model in TCD Dataset

Transcranial Doppler (TCD) is a non-invasive method for detecting ischemic stroke disease and is widely used in clinical diagnosis. Due to the obvious imbalance of medical data, oversampling is needed to balance it. Although the oversampling method is one of the important means to solve the problem of imbalanced data classification, the traditional oversampling method will inevitably introduce noise, which will affect the classification results. To address this issue, we proposed a new oversampling method combining the membership function (MF) and the variational autoencoder (VAE). Our method utilized VAE as a generative model to generate new samples to reduce the Imbalance Ratio (IR) of the dataset. Then the new samples are filtered using MF to weaken the impact of the noise introduced by the oversampling method on classification performance. In addition, in view of the weak adaptability of traditional MFs to complex sample distributions in practical applications, a new MF is proposed by using kernel function mapping and hypersphere. The classification experiments on TCD imbalanced dataset prove that the oversampling method we proposed has improved classification performance on multiple evaluation criteria compared with traditional oversampling methods.
EVALUASI SISTEM DRAINASE POLDER KOTA SEMARANG DILIHAT DARI SUDUT PANDANG KOTA RAWAN BENCANA BANJIR

One of the flood disasters that often hit Semarang City is caused by the lack of management of the drainage system which ultimately has great potential for the emergence of very adverse flood disasters. The condition of the drainage channel capacity that continues to decrease due to sedimentation, garbage, and poor maintenance also aggravates the flood problem in Semarang City. Flood management in Semarang City has been carried out with the development of polder system drainage in the Old City and Banger River areas of Semarang City. Based on these conditions, an analysis was carried out that aims to evaluate the concept of polder system drainage in Semarang City in solving flood problems. The method carried out in this study is to use a qualitative descriptive analysis of the flood resolution program through the polder system in Semarang City. The results of the analysis explained that the polder system had been built in accordance with existing principles and regulations but the lack of maintenance of the Polder Banger resulted in the polder function not being optimal. The concept of developing Banger polders in East Semarang was well developed based on considerations of safety, sustainability, and flexibility aspects.
Transferring 3D facial expressions from adults to children

Diagnosis of craniofacial conditions is shifting towards pre- and peri-natal stages, since early assessment has shown to be crucial for the effective treatment of functional and developmental aspects of children. 3D Morphable Models are a valuable tool for such evaluation. However, limited data availability on 3D newborn geometry, and highly variable imaging environments, challenge the construction of 3D baby face models. Our hypothesis is that constructing a bi-linear baby face model that allows identity and expression decoupling, enables to improve craniofacial and brain function assessments. Thus, given that adult and infants facial expression configurations are very similar and that 3D facial expressions in babies are difficult to be scanned in a controlled manner, we propose transferring the facial expressions from the available FaceWarehouse (FW) database to baby scans, to construct a baby-specific bi-linear expression model. First, we defined a spatial mapping between the BabyFM and the FW. Then, we propose an automatic neutralization to remove the expressions from the facial scans. Finally, we apply expression transfer to obtain a complete data tensor. We test the performance and generalization of the resulting bi-linear model with a test set. Results show that the obtained model allow us to successfully and realistically manipulate facial expressions of babies while keeping them decoupled from identity variations.
TEACHERS’ STRATEGIES IN TEACHING ENGLISH TO YOUNG LEARNERS

This study aims to examine and describe the strategies used by English language teachers in teaching young learners, specifically at Kumon School in Makassar Indonesia. The methods applied in this study are peer observations, and interview methods. The results showed that based on the fifteen principles of teaching English to young learners, the three teachers successfully conducted the fourteen of the principles. The last principle examined was inapplicable in the institution. This study also revealed that the main obstacle in teaching young learners at this institution were; the unequal number of students for each classroom at the same levels, students grouping especially in competitive activities and teachers preferences on teaching aids and facilities. The research is concluded that to ensure the successful teaching and learning process at the early stage or young learners, particularly at this English institution, the continuous teacher training programs; pre-service and in-service are one of the key element. Furthermore, teachers’ creativity of using and selecting teaching strategies in teaching young learners and school’s supports in the form of classroom facilities and teaching materials
Restricted boltzmann machine and softmax regression for acute respiratory infections disease identification

Restricted boltzmann machines (RBM) have attracted much attention lately after being proposed as building blocks of deep learning blocks. RBM is an algorithm that belongs to the artificial neural network (ANN) algorithm. Deep learning models can be used in the health field to identify diseases using medical data records. Acute Respiratory Infection (ARI) is a disease that infects the respiratory tract. A patient infected by ARI diseases is high. To identify ARI can use the symptoms that the patient had experienced. Based on this background, this study aims to help identify ARI disease using its symptoms. The method used for identification is the deep learning model, which was built using the RBM and softmax regression. Three steps were used in this research, which are training, testing, and implementation. The trained deep learning model will be implemented to identify ARI disease. This research will use ARI data from Puskemas Warungasem, Indonesia. From the research result, the deep learning model can get an accuracy of 96%. The deep learning configuration used in this research has 4 RBM layers, 1 Softmax layer as the output layer, and a learning rate value of 0.01 and 1000 iterations. This research can be used as a reference so that the next researcher can add other algorithms to Deep learning to improve accuracy.
Extending the Sustainability-Quality Model for supporting the design of Persuasive Software Systems

This research aims at providing a guiding support for the selection of relevant features and quality requirements for designing persuasive software systems. To do this, a mapping between the Persuasive System Design (PSD) model and a generic Sustainability-Quality (SQ) model was carried out. As a result of this mapping, we extended the SQ model, by adding certain types of relationships with specific features of the PSD model. A Graph database tool, named Neo4j, was used for facilitating the visualization of the identified relationships. And we also used the query language Cypher in order to retrieve data from the graph. Finally, we used an existing persuasive software system for illustrating the usefulness of the extended SQ model represented as graphs.
Pengamanan Dokumen Digital Perusahaan Menggunakan Metode Least Significant Bit (LSB) Dan Algoritma RC4 Stream Chipher

In today's digital era, where company documents are made in digital form, the issue of security and confidentiality of data or documents is an important aspect for companies. Steganography is a technique to hide messages or secret data in a place called a carrier file. While cryptography is a science that studies how to keep data or messages safe when sent, from sender to recipient without interference from other parties. The purpose of this study is to provide maximum security for company digital documents, using the Least Significant Bit (LSB) steganography method and the RC4 stream cipher cryptographic algorithm. The expected result of this research is to produce an application that can secure the company's digital documents