Reviewerly/exHarmony huggingface dataset:
Decision Support System for Performance Assessment in Raising Teacher Salaries Using the SAW Method at MA'ARIF Vocational School

The Decision Support System is a specialized information system designed to aid management in making effective and efficient decisions on semi-structured problems; it does not replace conventional decision-making procedures. The system that is currently running at the Cicalengka Integrated Ma'arif Vocational School has deficiencies: The process of determining teacher salary increases at the Cicalengka Integrated Ma'arif Vocational School takes more time due to the difficulty of finding data; It's easy to make mistakes because the salary increase process is done manually; It's easy to lose the raise process file. The decision-making function increase in teacher salaries at SMK Ma'arif Terpadu Cicalengka. This thesis uses a Decision Support System with the SAW (Simple Additive Weighting) method then the system development method uses OOAD (object-oriented analysis and design) with the RUP (Rational Unified Process) model. Rational Unified Process (RUP) is a software engineering technique created by compiling the software development industry's best practices. RUP is object-oriented, with activities centered on model development utilizing the Unified Modeling Language (UML). The final result of the Thesis and the making of applications and Decision Support Systems Assessing Performance for Salary Increases/Teachers at SMK Ma'arif Terpadu Cicalengka is a website-based application that can facilitate the process of evaluating performance for salary increases at SMK Ma' Arif Integrated Cicalengka, The Decision Support System built using the SAW method is expected to make it easier for schools to evaluate performance for teacher/manager salary increases; The Decision Support System that is built can minimize errors that occur from the performance appraisal process for teacher/manager salary increases; Can reduce the loss of archives at Integrated Cicalengka Ma'arif Vocational High School.
Prediction Model for Covid-19 Cases in Indonesia Using Linear Regression and Polynomial Regression Methods

The World Health Organization on March 11, 2020, declared Coronavirus Disease 2019 (Covid-19) a pandemic. Covid-19 is a disease caused by a new type of coronavirus, namely Sars-CoV-2, which affects the respiratory system. Until now, positive confirmed cases of Covid-19 in Indonesia are still occurring every day. This study aims to predict the addition of Covid-19 cases in Indonesia. The data is sourced from the public API page covid19.go.id in the form of an additional number of Covid-19 cases in Indonesia by 122 lines of data. Predictions are made using linear regression and polynomial regression methods as comparisons. Evaluation of the linear regression method obtains a value of R2 = 0.57, while the polynomial regression method obtains a value of R2 = 0.84. Based on these evaluations, the polynomial regression method yields better results than the linear regression method. The prediction of Covid-19 cases in Indonesia from January to March 2022 using the polynomial regression methods predicts that the addition of Covid-19 cases will rise again.
Sentiment Analysis Of Vaccination Using The K-Nearest Neighbor Algorithm

The outbreak of the COVID-19 virus that attacked the country of Indonesia made the government implement a policy, namely giving vaccinations. Since the announcement of the government's policy on administering the COVID-19 vaccine in January 2021, there have been a lot of discussions, especially on social media. One of the social media that is widely used by the public for opinions is Twitter. this has resulted in Twitter becoming a medium for expressing people's thoughts on administering the COVID-19 vaccine. Opinions generated can be positive, negative, or neutral towards vaccine administration. Based on the description of the problem, the study aims to analyze the sentiments of netizens regarding government policies in administering vaccinations. The method used is the KNN Algorithm to carry out sentiment analysis through public opinion on Twitter social media. The results sentiment with the highest value is positive which has more than 150 sentences. Then it produces an accuracy of 86.6% Precision of 85% and Recalls of 81%.
Malaria Outbreak Investigation in Nensebo District, West Arsi Zone, Southeast Ethiopia: A case-control study

Background: Malaria-related morbidity and mortality are 94% concentrated in Africa. Ethiopia is one of ten African countries affected by malaria, with 60% of the population living in malaria-risk areas. Recently, seasonal outbreaks have been reported in all regions, including previously malaria-free areas. Nationally, the Nensebo district of the west Arsi zone is classified as having very low transmission. During the 21st WHO week of 2021, Melka Denbi kebele reported an unusually high number of malaria cases to this district. The purpose of this study was to look into the magnitude of the malaria outbreak and the factors that contributed to it. Methods: A descriptive study was followed by an unmatched case-control study on 86 cases and 172 controls who were chosen at random. Malaria cases were those who were confirmed positive by rapid diagnostic test (RDT) and were line-listed at a health facility, while controls were those who lived nearby and were confirmed negative by RDT. At a p-value of 0.05 and a 95% confidence interval, logistic regression was used to identify malaria contracting factors.Results: With a mean age of 22 (12.31SD), the overall attack rate was 20.2/1000. Plasmodium vivax (PV) 105 (52.8%) was the most common. Staying out at night (AOR=3.94; 95%CI: 2.18-7.37) and stagnant water/intermittent river within 1 km of the vicinity were risk factors. Screened houses were protective (AOR=0.49; 95%CI: 0.27-0.89), as was knowledge of malaria transmission (AOR=0.51; 95%CI: 0.28-0.93) and prevention and control methods (AOR=0.50; 95%CI: 0.27-0.93).Conclusion: The illness was caused primarily by PV species known for their relapsing characteristics. Risk factors included stagnant water near homes and sleeping outside at night. Malaria screening centers and increased public awareness reduce the risk of contracting the disease. Our recommendations included regular environmental monitoring, behavioral change communication, ensuring radical cure, and further research with a detailed entomological survey and climate variables.
PUBLIC SENTIMENT ANALYSIS THROUGH TWITTER ON SALES TAX ON LUXURY GOOD INCENTIVES USING MACHINE LEARNING METHOD

The government’s policy on Incentives on Sales Tax on Luxury Goods on the Delivery of Taxable Goods Classified as Luxury in the Form of Certain Motorized Vehicles Covered by the Government for the 2021 fiscal year during the pandemic has invited pros and cons among the public, including twitter users. This study aims to identify and analyze public sentiment toward the government’s policy. The method used is machine learning, namely the Natural Language Toolkit (NLTK) analysis. The Results of this study obtained 49 positive sentiments and 65 negative sentiments that describe support, opposition and critical discourse on the policy with a data accuracy rate of 94.23%. The government needs to consider public sentiment and can be taken into consideration in determining the impact of a policy on the economy from all walks of life.
Virtual Reality (VR) Serious Game Design Framework and Behaviorism Factors for Real Life Adaptation Skills Training for Children with Autism Spectrum Disorders(ASD)

A serious game applying virtual reality (VR) is expected to be used as a tool for independent life training by inducing immersion and interest in children with Autism Spectrum Disorders(ASD). This study is to examine a framework for the development of a Virtual Reality-Serious Game(VR-SG) for training ASD children's real life adaptation skills through literature review, and to propose a behaviorist prototype for synchronizing VR-SG. The results of the study are as follows. First, the VR-SG framework approaches in three areas: pedagogy, learning content and game mechanism, and evaluation. Second, the behaviorist strategy for synchronizing VR-SG for real life adaptation skills training for students with ASD is to: (1) Clear application of reinforcement and punishment; (2) Induce continuity of participation in the game by applying the reinforcement plan; (3) diversification of behavioral chains for repetitive practice; (4) difficulty control using stimulation promotion; (5) diversification of reaction promotion according to personal preference. This study presented basic contents for use in the development of independent life training programs for children with ASD by studying the framework for developing functional virtual reality games for children with ASD.
COLOR IMAGES COMPRESSION USING VECTOR QUANTIZATION (VQ)TECHNIQUE WITH DIFFERENT MATHEMATICAL TRANSFORMATIONS

Image data compression is one of the most important techniques in digital image processing that used to reduce the size of image data with taking into account getting good quality of the compressed image, before several decades the researchers produced a lot of coding methods to decrease the image size, these methods include loss data compression and lossless data compression. in this research vector quantization techniques have been used to compress color image, firstly vector quantization has been applied using only cosine transform and then using principle component transform together with cosine transform, secondly vector quantization (VQ) has been achieved using wavelet transform only and then using the same transform with the principle component transform, many statistical properties have been shown and many fidelity criteria have been computed. This manuscript also includes the theory concepts of Vector Quantization, cosine transformation, principle component analysis and wavelet transform.
E-Commerce Website having a Niche in Books

Abstract: Everyone knows how expensive schools, colleges, and tuition fees are today. One such expense that everyone unconsciously ignores, is the high cost of college and school textbooks. Buying and selling used books have been the latest thing in the present. The main motive of this paper is to provide the masses, with a way to sell and buy used books Online. In this paper, we will present the working of our website and the intention behind it. Our website, Pustak.com is going to be an online platform that specializes in purchasing and selling second-hand used books. It will be a customer-to-customer platform to buy and sell books. Our website will help customers to browse through a catalog of books. Finally, We want to create an ecosystem where every Reader gets the book he/she wants.
Automated Financial Documents Processing System Using Machine Learning

Abstract: In today’s world technology has affected each sector. But still, within the banking sector, we tend to face several problems. Bank handles giant volumes of cheques within the clearing method. The method involves several technical verifications as well as signature verification. A number of these steps are manual and need human intervention to finish the technique. This method needs high human capital preparation and longer interval. Fallacious practices are one in every one of the foremost problems plaguing banks in recent times. Hiring a lot of folks to manage the cheque verification method does not solve this issue.
Engaging With Vulnerability

Background and Objective: Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general and effective strategy to improve DNN robustness (i.e., accuracy on noisy data) against adversarial noises. However, DNN models trained by the current existing adversarial training methods may have much lower standard accuracy (i.e., accuracy on clean data), compared to the same models trained by the standard method on clean data, and this phenomenon is known as the trade-off between accuracy and robustness and is commonly considered unavoidable. This issue prevents adversarial training from being used in many application domains, such as medical image analysis, as practitioners do not want to sacrifice standard accuracy too much in exchange for adversarial robustness. Our objective is to lift (i.e., alleviate or even avoid) this trade-off between standard accuracy and adversarial robustness for medical image classification and segmentation.Methods: We propose a novel adversarial training method, named Increasing-Margin Adversarial (IMA) Training, which is supported by an equilibrium state analysis about the optimality of adversarial training samples. Our method aims to preserve accuracy while improving robustness by generating optimal adversarial training samples. We evaluate our method and the other eight representative methods on six publicly available image datasets corrupted by noises generated by AutoAttack and white-noise attack.Results: Our method achieves the highest adversarial robustness for image classification and segmentation with the smallest reduction in accuracy on clean data. For one of the applications, our method improves both accuracy and robustness.Conclusions: Our study has demonstrated that our method can lift the trade-off between standard accuracy and adversarial robustness for the image classification and segmentation applications. To our knowledge, it is the first work to show that the trade-off is avoidable for medical image segmentation.
Strength of Quick Response Code in Smart Billing System

Abstract: The daily grocers and retail establishments are the main focus of Smart Billing System. In order to decrease store thefts and improve the effectiveness of the billing system, smart billing systems employing RFID operate on a similar basis. When a consumer wants to purchase a product, they must first scan the QR code located on the back of the item. This will take them to ourapp, where they may view more information about the product and select whether or not to go to checkout.
MOTION CAPTURE WITH MEMS SENSORS

The object of this article is the registration and analysis of human movements based on sensors. This paper presents a comparison of the basic methods of data processing from inertial micromechanical sensors to collect data a device was implemented that captures movements. As result the device uses the motion data from accelerometer and gyroscope to calculate the motion trajectory: the angle of rotation and acceleration. The data is read by the microcontroller, after which it is filtered and processed by one of the filters (Complementary, Kalman), and finally transferred to a computer for further analysis and display. The purpose of the article is to compare several methods of data processing from microelectromechanical. The results obtained: device was developed, obtained data that can be used to characterize the methods and analyze their work in the system. Conclusions: In the course of the study, a device was developed for collecting and processing data from MEMS sensors, which showed the effectiveness of the complementary filter in comparison with the Kalman filter in real-time systems with limited computing power. Real results confirmed that the results of the complementary method using less computational resources are not far behind the more costly Kalman filter without the use of auxiliary sensors, like a digital compass.
The semiproximal SVM approach for multiple instance learning: a kernel-based computational study

Abstract The semiproximal Support Vector Machine technique is a recent approach for Multiple Instance Learning (MIL) problems. It exploits the benefits exhibited in the supervised learning by the Support Vector Machine technique, in terms of generalization capability, and by the Proximal Support Vector Machine approach in terms of efficiency. We investigate the possibility of embedding the kernel transformations into the semiproximal framework to further improve the testing accuracy. Numerical results on benchmark MIL data sets show the effectiveness of our proposal.
Exploring Factors Influencing AI Sentiment-Analysis Engine Robot Use - Surveying Students in Social Science College

To understand the factors influencing AI engine robot use, this study developed a conceptual model along the lines of previous research.The research model hypothesized that personal innovativeness in Information Technology (new software, new hardware, new AI engine, new chatbot, use new technology), perceived usefulness of Information Technology and perceived ease of use of Information Technology positively affect attitude toward AI engine robots, which in turn facilitates AI engine robot intention and AI engine robot use.By collecting data from 55 surveys from college Student's respondents who have employed AI engine robots, we applied statistics to test the relationships in the model.Our findings demonstrate a positive effect of personal innovativeness in Information Technology, perceived usefulness of Information Technology and perceived ease of use of Information Technology on attitude toward AI engine robots.In addition, attitude toward AI engine robots has a positive effect on AI engine robot intention and AI engine robot use.Students use the AI engine to score the natural language description of the copywriting.Future research in the field of natural language processing and natural language understanding, using several AI engines for mathematical operations and applications.Quantitative research on natural language processing and natural language understanding.Used to solve more complex NLP and NLU and sentiment analysis problems.
Understanding Models and Model Bias with Gaussian Processes

Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination.How can we understand the behavior and potential biases of these models, especially if our access to the underlying model is limited?We argue that counterfactual reasoning is ideal for interpreting model behavior, and that Gaussian processes (GP) can provide approximate counterfactual reasoning while also incorporating uncertainty in the underlying model's functional form.We illustrate with an exercise in which a simulated lender uses a biased machine model to decide credit terms.Comparing aggregate outcomes does not clearly reveal bias, but with a GP model we can estimate individual counterfactual outcomes.This approach can detect the bias in the lending model even when only a relatively small sample is available.To demonstrate the value of this approach for the more general task of model interpretability, we also show how the GP model's estimates can be aggregated to recreate the partial density functions for the lending model.
The Nature and Emergence of the Língua Geral Amazônica according to Mufwene’s Language Ecology Model

This paper addresses two aspects of the Língua Geral Amazônica (LGA). First, we propose that Língua Geral as a technical term in Portuguese was inspired by administrative practices in Spain’s American colonies and that, given contemporary usage, the term should be understood functionally as any Tupi-Guarani variety broadly mutually comprehensible with the colonial Old Tupi, rather than as a structurally modified variety or the speech of a particular ethnic or social group, as has been claimed. We then briefly analyse two recent hypotheses that treat the emergence of LGA as creolisation (VIEIRA; ZANOLI; MÓDOLO, 2019; NOBRE, 2019). Next, we apply key notions of the Language Ecology creolisation model (MUFWENE, 2003, 2008) to the formation of LGA and suggest a periodization.
Internet of Things (IoT) Device Investigative Analysis Using Machine-to-Machine (M2M) Framework

As we know that now a days the possibility of the uninterrupted attacks on the IOT devices are increasing. The less memory and the minute process- ing power of these appliances make it tough for the security analyst to store the records of the different attacks. The forensic analysis is used to evalu- ate the damage done on the devices due to numerous attacks. In this mecha- nism the attacks on the IOT devices are detects undoubtedly by using machine- to-machine (M2M) framework. In addition to the using machine-to-machine framework the machine learning algorithms also been used to identify various attacks automatically. Here we use the third-party logging server in order to issue. The execution will be studied in the form of accuracy, precision and the Random Forest gives the most accuracy.
PENINGKATAN KEMAMPUAN MEMBACA PEMAHAMAN MELALUI MODEL PEMBELAJARAN COOPERATIVE INTEGRATED READING COMPOSITION (CIRC) PADA KELAS 5 SD NEGERI 096 SARIJADI SELATAN TAHUN AJARAN 2022/2023

The purpose of this research was to improve ability of reading comprehension on fifth grade student of 096 Sarijadi Selatan Primary School in academic year of 2022/2023. The type of this research is Classroom Action Reasearch (CAR). This research was done in two cycles. Each cycle consisting of four phases there were planning, acting, observing, and reflecting. Subject of this research was 27 fifth grade student. Data collecting technique used observation, interview, test, and document study. Based on the research findings and the observation of Minimum Mastery Criteria (KKM) namely 75, the average score of students’ ability of reading comprehension on first test before the action done was 60,56. After cycle one, the score increased to 72,22 and at cycle 2 it became 80. The conclusion of this research was the impelementation of Cooperative Integrated Reading Composition (CIRC) model can improve students’ ability of reading comprehension on fifth grade of 096 Sarijadi Selatan Primary School Bandung City in academic year of 2022/2023.
Rainless Day Management using Geographic Information System and Wireless Sensor Networks on Climatology Station Banjarbaru

Information obtained at the Banjarbaru Climatology Station includes seasonal forecasts, rainfall analysis, rainfall properties analysis, rainy day analysis, and monitoring of rainless days in the South Kalimantan region. Monitoring of Rainless Day is a form of data on rainless days that occur in South Kalimantan in sequence. The data is sent by observers to the Banjarbaru Climatology Station office using SMS, WhatsApp, and even by calling the staff of the Banjarbaru Climatology Station so that employees must check every media both SMS, WhatsApp, and telephone to find out the data sent by observers. Related to these problems, a web-based rainless day monitoring application was made. By using the Research and Development (R&amp;D) method to facilitate the management of rainless day data, a geographic information system management system using Google Maps API and MySQL database is built which is integrated with Internet of Things (IoT) technology based on Wireless Sensor Networks (WSN) for data collection in real-time. The system built will provide notifications in the management of areas affected by rainless days in order to deal with drought.