Reviewerly/exHarmony huggingface dataset:
Butik Nauli, Reorder Point (ROP) Rancang Bangun Sistem Informasi Pengendalian Inventory Berbasis Website (Studi Kasus : Butik Nauli)

Nauli boutique is located in Pekanbaru city. In this boutique there are problems that often occur, namely, the supply of goods. In managing inventory, losses often occur due to excess or lack of goods. This causes the stock of goods to not be counted clearly because they do not have records of sales and inventory data. When you want to order goods from a supplier, the boutique owner only uses his estimates. To solve this problem, an inventory system was created using the Reorder Point (ROP) method. The Reorder Point (ROP) method is used to determine the limit of stock items in the boutique. This system also uses the Economic Order Quantity (EOQ) method, which functions to determine the minimum number of items that must be filled again. To find out the feasibility of this system, black box testing and User Accepted Testing were carried out. Based on the results of black box testing, it can be concluded that the features provided by the inventory system that has been built can run well and as desired. For the results of the User Accepted Testing test, the results show that the system is running according to its function. Based on the User Accepted Testing test, the results show that the system that has been built is very helpful for users in controlling inventory and managing transactions in the boutique, this is evidenced by the results of Likert scale calculations. The conclusion from the tests that have been carried out is that all the features contained in the system are as expected and can be accepted and implemented properly.
Authorship attribution of Morsi Gameel Aziz’s lyrics: A clustering-based stylometry approach

Numerous studies have addressed the issue of the authorship of Morsi Gameel Azizâ€™s lyrics. These studies have traditionally been based on chronological criteria for determining the real authors of disputed lyrics. To date, there is no agreement on the real authors of these disputed lyrics. This can mainly be attributed to both selectivity and the lack of empirical evidence in such studies, raising questions about the reliability of such approaches. With the advent of machine learning systems and data mining techniques, it is now possible to process thousands of texts using replicable methods. Thus, this study seeks to address the issue of the authorship of Morsi Gameel Azizâ€™s lyrics making use of these advances by applying a clustering-based stylometry approach. The hypothesis is that lyrics grouped or clustered together are more likely to be written by the same poet. A corpus of 1,089 lyrics was built, including all known lyrics attributed to Aziz and the lyrics of the poets thought to be the real authors of the disputed lyrics. The lyrics were clustered using the Gibbs sampling Dirichlet multinomial mixture (GSDMM) technique, and were assigned to 4 main classes, with the 12 disputed lyrics clustered within Azizâ€™s class. Based on this, it is clear that the GSDMM model is effective and reliable in clustering short documents in Arabic. The results of the study show that machine learning systems and stylometric authorship techniques can be used in resolving many authorship questions that remain controversial and unanswered in Arabic literature.
Analysis of 3D Facial Shapes of Female Adult to Improve Face Mask Fit

About this reportThe 2019 World Air Quality Report is based on data from the world's largest centralized platform for real-time air quality data, combining efforts from thousands of initiatives run by citizens, communities, companies, non-profit organizations and governments.Through aggregating, validating and visualizing real-time data from governments and sensors operated by individuals and organizations, IQAir strives to raise awareness of air pollution, allowing people to take action to improve air quality and protect their health.The 2019 World Air Quality Report is based on a subset of the information provided through the platform.It includes only PM2.5 (fine particulate matter) data as acquired from ground-based air quality monitoring stations with high data availability.An extended presentation of the world's most polluted cities during 2019 is available online, allowing further exploration of air quality across different regions and subregions in 2019 in an interactive format.Live and forecast air quality information for all included locations can also be explored through the IQAir Air Quality Map, which presents a real-time overview of the world's air quality data in one place.1In 2018 coal accounted for 59% of China's direct energy consumption.This also represented over half of the world's total coal use (50.5%).(BP, 2019)
Predicting Safety Information of Drugs Using Data Mining Technique

Data Classification is the application of data mining techniques to discover patterns from the micro array and biological datasets. This research entitled “PREDICTING SAFETY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE” incorporates information theory, which is the process of deriving the information from the unsupervised dataset through feature selection. Finding the best features that are similar to a test data is challenging task in current data era. This research presents a framework for discovering best feature selection from unsupervised datasets. The proposed research work presents a new approach to measure the features (attributes) in drug prediction dataset using the methodologies namely, data cleaning, Adaptive Relevance Feature Discovery and Random Forest Classification. There are number of pharmacy companies are available in the market with multiple medicines for same problem.This prediction of drugs is used to prescribe the medicines for the patient’s disease by analyzing the history of the patient’s health. Feature selection and dimensionality reduction is characterized by a regularity analysis where the feature values correspond to the number times that term appears in the dataset. The relevance feature discovery method gives a useful measure is used to find the similarity features between data points are likely to be in terms of their features property. Some of the challenges faced in finding the best feature selection include positive, negative and inconsistency. This Proposed work proposes an enhanced Drug prediction based on Random Forest classification method to estimate the feature searching that is measured using minimal redundancy optimization method corresponding to drug prediction dataset.
THE IMPORTANCE OF A SIMPLE COMPUTER GAME WHEN CREATING SORTING ALGORITHMS

V. Stoffová1, L. Végh2 1Trnava University, Faculty of Education (SLOVAKIA) 2J. Selye University in Komárno, Faculty of Economics and Informatics (SLOVAKIA)
Implementasi Fuzzy Logic untuk Sortasi Buah Tomat Secara Otomatis

Tomatoes are a type of vegetable that produces fruit. Ripe tomatoes will be bright red witha weight above 80 grams. Tomatoes can be found in the center of traditional shoppingmarkets and wet markets. The traders get tomatoes from farmers in large quantities sothat tomatoes are quite varied. There is a small red, there is a large tomatoes but not yetperfectly ripe and so forth. This is quite troublesome for traders in selling tomatoesbecause they have to be sorted first. To overcome this problem, we need a decisionmaking system to determine the quality of tomatoes and sort them. In this research, thedecision-making system to determine the quality of tomatoes using fuzzy logic Mamdanimethod and sorting tomatoes using Arduino-based conveyer. The result of the researchcan be demonstrated with the MATLAB application and manual count. The MATLAB andmanual results show no difference. The best quality tomatoes are at value 8 with red colorand large size. While the poor quality of tomatoes is at value 2 with green color and largesize. Green tomatoes are certainly not yet ripe and therefor the quality of tomatoes is stillpoor.
Web Application Security Threats and Analysis of Vulnerability Scanners

In recent years, web security has been regarded in terms of protecting the web application layer from unauthorized users' attacks.As our reliance on technology grows exponentially, security becomes increasingly important.The main objective of this paper is to provide knowledge about web application layer vulnerabilities, their prevention methods and to perform a comparison of the latest tools and mechanisms used to detect these threats and vulnerabilities.
SIZE, AGE AND LEVERAGE OF NIGERIA QUOTED OIL AND GAS CORPORATIONS

Firm size and age influence firm-level leverage. The extent of such influence on the oil and gas industry is not known in Nigeria. There are very few empirical studies that interrogate the effects of firm size and listing age on leverage in Nigeria. This study examines the impacts of firm size and listing age on firm-level financial leverage of listed oil and gas companies in Nigeria. It was non-experimental research and correlational in nature. Data were extracted from annuals and accounts of 8 firms over a period of 13 years (2007-2019) and subjected to descriptive statistics (number of observations, mean, standard deviations, mean, minimum and maximum means) and inferential statistics (multiple regression analysis). The findings show that firm size has a negative and significant impact on firm-level financial leverage. Firm age has a positive and significant effect on firm-level leverage. In this paper, we contribute to the literature by examining the presence and direction of firm size and listing age to financial leverage user data from listed oil and gas firms in Nigeria. Our study is the first to address the adverse implications of Modeling with firm size and listing age on firm-level financial leverage.
Simpson's Paradox: A Singularity of Statistical and Inductive Inference

The occurrence of Simpson's paradox (SP) in $2\times 2$ contingency tables has been well studied. The present work comprehensively revisits this problem using a combination of philosophical reflections, causal considerations, and probability theory. The first contribution is to provide a schematic analysis of SP in $2\times 2$ contingency tables and present new results, detailed proofs of previous results and a unifying view of the important examples of SP that have been reported in the literature. The second contribution of the paper suggests a new perspective on the surprise element of SP, raises some critical questions regarding the influential causal analyses of SP and provides a broad perspective on logic, probability, and statistics with SP at its center. The upshot of this research is that we need both causal concepts and statistical tools coupled with philosophical analyses to sort out issues regarding SP.
Analysis on Ontology and Semantic Web Technique in Education

Semantic web technologies would heavily rely on formal ontology in order to structure data, thus the data provide comprehensive and transportable understanding of machine language. Ontology which serves as metadata scheme that provides a set of vocabulary concept. Ontology would provide an important role in modelling education platform. Thus in this paper an analysis on education platform using ontology and semantic web technique is studied.
Метод Мюррей як засіб психологічної реадаптації постнаркозалежних дорослих

International scientific and practical conference «Pedagogy and Psychology in the Modern World: the art of teaching and learning» : conference proceedings, February 26–27, 2021. Riga, Latvia : «Baltija Publishing». 444 pages.
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey

After an artificial model background subtraction, the pixels have been labelled as foreground and background. Previous approaches to secondary processing the output for denoising usually use traditional methods such as the Bayesian refinement method. In this paper, we focus on using a modified U-Net model to approximate the result of the Bayesian refinement method and improve the result. In our modified U-Net model, the result of background subtraction from other models will be combined with the source image as input for learning the statistical distribution. Thus, the losing information caused by the background subtraction model can be restored from the source image. Moreover, since the part of the input image is already the output of the other background subtraction model, the feature extraction should be convenient, it only needs to change the labels of the noise pixels. Compare with traditional methods, using deep learning methods superiority in keeping details.
READERS' INTERESTS AND VALUE ORIENTATIONS OF TEENAGERS

This article attempts to identify the interests and values of modern teenagers who live in a large peripheral city and attend events that are regularly held in municipal libraries. The main possible types of work of a modern library and secondary school are not only the recommendation of educational and extracurricular literature, events, Olympiads and competitions, but also the education of library culture among schoolchildren. The library is forced to infiltrate the school to keep up with school education. To do this, it is necessary to organize joint work with representatives of specific schools (their principals, head teachers, interested teachers and parents), as well as with higher-level organizations of municipal and regional levels. It was supposed to find out what is the basis of the attitude of adolescent schoolchildren to reading as a process and as a value. The article is based on the eponymous research conducted by the Izhevsk city libraries in 2019. The study showed a fairly high degree of interest of modern schoolchildren in the services that can be provided in municipal libraries. The main topics of information needed by respondents were identified. We also managed to determine the preferred genres of books. Respondents named their favorite authors and expressed their attitude to reading and reading people. Thanks to the results obtained, librarians are able to improve the culture of library services for library visitors of different socio-demographic groups and plan their professional activities more carefully.
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques

Human Activity Recognition (HAR) is considered a valuable research topic in the last few decades. Different types of machine learning models are used for this purpose, and this is a part of analyzing human behavior through machines. It is not a trivial task to analyze the data from wearable sensors for complex and high dimensions. Nowadays, researchers mostly use smartphones or smart home sensors to capture these data. In our paper, we analyze these data using machine learning models to recognize human activities, which are now widely used for many purposes such as physical and mental health monitoring. We apply different machine learning models and compare performances. We use Logistic Regression (LR) as the benchmark model for its simplicity and excellent performance on a dataset, and to compare, we take Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). Additionally, we select the best set of parameters for each model by grid search. We use the HAR dataset from the UCI Machine Learning Repository as a standard dataset to train and test the models. Throughout the analysis, we can see that the Support Vector Machine performed (average accuracy 96.33%) far better than the other methods. We also prove that the results are statistically significant by employing statistical significance test methods.
Understand Online Medical Consultation Based on Dialogue State Tracking

With the continuous development of online clinical inquiry system, Chinese medical online inquiry community emerges actively. One of the core task of medical inquiry system is to extract the medical information from dialogue with specific significance for treatment including self-reported diseases, treatment means, doctor's advices and other kind of useful entities. However, due to the specific difficulties, such as colloquialism in dialogue text, it is still difficult to extract key medical information exactly. To alleviate this issue, this paper proposes a new neural network based method of information extraction for medical online consultation system with a coarse-granularity data annotation approach, which is more time-saving and robust compared with the traditional sequence labelling methods. Experimental results prove the effectiveness of our method.
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction -- an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA^2) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA^2) layer is proposed to rectify attention in temporal domain, leading to benefits with motions scaled in a larger range. Extensive analysis and experiments verified the motivation and significant performance gains of our method, dubbed as Flow Estimation via Spatial-Temporal Attention (FESTA), when compared to several state-of-the-art benchmarks of scene flow estimation.
Enabling Replications and Contexts in Reversible Concurrent Calculus

Existing formalisms for the algebraic specification and representation of networks of reversible agents suffer some shortcomings. Despite multiple attempts, reversible declensions of the Calculus of Communicating Systems (CCS) do not offer satisfactory adaptation of notions that are usual in ''forward-only'' process algebras, such as replication or context. They also seem to fail to leverage possible new features stemming from reversibility, such as the capacity of distinguishing between multiple replications, based on how they replicate the memory mechanism allowing to reverse the computation. Existing formalisms disallow the ''hot-plugging'' of processes during their execution in contexts that also have a past. Finally, they assume the existence of ''eternally fresh'' keys or identifiers that, if implemented poorly, could result in unnecessary bottlenecks and look-ups involving all the threads. In this paper, we begin investigating those issues, by first designing a process algebra endowed with a mechanism to generate identifiers without the need to consult with the other threads. We use this calculus to recast the possible representations of non-determinism in CCS, and as a by-product establish a simple and straightforward definition of concurrency. Our reversible calculus is then proven to satisfy expected properties, and allows to lay out precisely different representations of the replication of a process with a memory. We also observe that none of the reversible bisimulations defined thus far are congruences under our notion of ''reversible'' contexts.
The effects of augmented reality-facilitated mobile game-based learning on the diversity of life for promoting learning at the natural history museum

The purpose of this study was to evaluate the effectiveness of Augmented Reality-facilitated Mobile Game-based learning (MGAR) approach by comparing the learning achievement, learning motivation and cognitive load of the students who learned with this approach with those learning with the conventional Mobile Game-based Learning (MGL), Augmented Reality-based Learning (ARL), and conventional Web-based Learning (WL). All units were consistently designed for the concept of the diversity of life. The participants were 192 grade 9 students who were randomly assigned into four groups: MGAR (N = 46), MGL (N = 50), ARL (N = 43), and WL (N = 53). The instruments were the pre-test and post-test for achievements, the learning motivation questionnaire, the cognitive load questionnaire, and a semi-structured interview. The overall results show that MGAR was more effective in enhancing students' understanding of the diversity of life and significantly more motivational than the other groups. In addition, the MGAR decreased the cognitive load of the students when compared with the other groups.
Adaptation of Parallel Framework to Solve Traveling Salesman Problem Using Genetic Algorithms and Tabu Search

The modeling of combinatorial optimization problems has acquired great importance both for research in mathematical processes and for companies starting new projects and seeking to identify the most efficient, beneficial and economic implementation. One of the most investigated combinatorial problems in optimization studies is the traveling salesman problem (TSP). Here we describe a parallel strategy to solve TSP problem using genetic algorithm and Tabu search based on framework(Guzman, N. Ruiz, Ardila, Jabba and Nieto, 2016) which make a solution through parallel processing within a Master-Slave structure. In addition, the proposed approach is compared with an existing algorithm.
PLAN-B: Predicting Likely Alternative Next Best Sequences for Action Prediction

Action prediction focuses on anticipating actions before they happen. Recent works leverage probabilistic approaches to describe future uncertainties and sample future actions. However, these methods cannot easily find all alternative predictions, which are essential given the inherent unpredictability of the future, and current evaluation protocols do not measure a system's ability to find such alternatives. We re-examine action prediction in terms of its ability to predict not only the top predictions, but also top alternatives with the accuracy@k metric. In addition, we propose Choice F1: a metric inspired by F1 score which evaluates a prediction system's ability to find all plausible futures while keeping only the most probable ones. To evaluate this problem, we present a novel method, Predicting the Likely Alternative Next Best, or PLAN-B, for action prediction which automatically finds the set of most likely alternative futures. PLAN-B consists of two novel components: (i) a Choice Table which ensures that all possible futures are found, and (ii) a "Collaborative" RNN system which combines both action sequence and feature information. We demonstrate that our system outperforms state-of-the-art results on benchmark datasets.