Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Diagnosa Penyakit Pada Tanaman Seledri Menggunakan Metode ANFIS

Expert systems are computer-based information systems that use expert knowledge to achieve high-level decision performance in a narrow problem domain. MYCIN, developed at Stanford University in the early 1980's for medical diagnosis, is generally considered the most well-known expert system. Celery (Apium Graveolens L) is a leaf vegetable and medicinal plant commonly used as a cooking spice. Celery is a versatile plant, especially as a vegetable and medicine and is also used as a salad. plant disease is one of the factors that can cause crop production failure. Celery disease can attack all stages of celery development, from seed production to plant production. In this study the authors used the ANFIS method. ANFIS is an integrated fuzzy system interface mechanism that is described in a neural network architecture. The fuzzy inference system used is the first order Tagaki-Sugeno-Kang (TSK) inference system with considerations of simplicity and ease of computation. ANFIS is an adaptive system based on fuzzy logic inference that combines two methods, namely adaptive (neural method) and fuzzy method. This adaptive network is used to adapt the fuzzy logic inference system to represent the desired fuzzy interference system. The principle of the working method is to combine two methods, namely the adaptive method and the fuzzy method in which both use the same two information sources, but have different representations.
Subject guides: Theses: Monash theses

This guide is designed to assist with locating theses either from Monash or from other institutions around the world.
Educational video and its uses on You Tube

A proposal for the design of a distance learning activity on the pedagogical use of YouTube is described. For the training of 27 teachers from Mid High Education of the University of Guadalajara in collaboration with the Virtual University of Guadalajara, to deliver the course we used the Google Classroom platform and the social network Facebook. The course aims to train teachers to understand and make use of technological tools that enable distance education. The proposal describes and highlights the potential of video and pedagogical use of youtube in times of the COVID-19 pandemic as a means to strengthen distance learning. For the data analysis, the videos and final reflections of each participant were reviewed, to recognize, guidelines that emerge from the data that generate sense looking for themes that could give a clue to organize the data. The recurrent and the particular constituted elements of inquiry. When the teachers were developing the videos, they modeled the contents, taught and selected the most appropriate codes for their recipients to communicate the concepts, processes, and this somehow conditions the Learning of students. Recognizing the role of the audiovisual broadcaster has implications by recognizing them as a mediator because it is a teacher who has to appropriate the content and is in the process of appropriating the technological tools are used to communicate such content, but also, is appropriating a model of communicating the educational through audiovisual media.
Implementasi Algoritma FP-Growth Pada Pengenalan Pola Penjualan

Data Mining is a series of processes to extract additional value in the form of information that has been known manually from a database by extracting patterns from data with the aim of manipulating data into more valuable information obtained by extracting and extracting patterns important or pull the data contained in the database. Data Mining technique has various kinds of algorithms and techniques for solving problems related to data, one of which is the FP-Growth algorithm and the association rule technique. The information displayed using the association rule technique, which displays the value of support and confidence from each data mining process. With the FP-Growth algorithm, it is proven from the results of the tests carried out, the FP-Growth algorithm and the association rule technique run as expected and can be implemented in this Data Mining
PENINGKATAN PENGUASAAN KOSAKATA BAHASA ARAB DENGAN MENGGUNAKAN MODEL MEMORISASI

This study aims to improve the mastery of Arabic vocabulary by using the memorization model. This type of research is a classroom action research conducted in three cycles. The subjects of this study were all students of class X-D of MAN 2 Pangandaran in the 2018/2019 academic year totaling 27 students. Data obtained from: student learning outcomes, student activity observations, student response journals, and observer field notes. The results showed that teacher activity increased from 2.49 in cycle I, to 2.92 in cycle II and 3.64 in cycle III. This means that the process of teaching vocabulary using the memorization model is very effective. The results of research on student activity increased from 2.37 in cycle I, 2.87 in cycle II, and 3.5 in cycle III. This means that students are very enthusiastic about the process of teaching vocabulary using the memorization model. The results of student journals have increased from 97.22% in cycle I, 98.14% in cycle II, and 100% in cycle III. This means that the process of teaching vocabulary using the memorization model received a positive response. The completeness of learning Arabic vocabulary in each cycle has increased. This can be seen from the test results, cycle I 85.18%, cycle II, 96.26%, cycle III 100%. The results showed that the memorization model could improve the learning outcomes of students' Arabic vocabulary mastery of class X-D MAN 2 Pangandaran.
Provenance for the description logic ELHr

We address the problem of handling provenance information in ELHr ontologies. We consider a setting recently introduced for ontology-based data access, based on semirings and extending classical data provenance, in which ontology axioms are annotated with provenance tokens. A consequence inherits the provenance of the axioms involved in deriving it, yielding a provenance polynomial as an annotation. We analyse the semantics for the ELHr case and show that the presence of conjunctions poses various difficulties for handling provenance, some of which are mitigated by assuming multiplicative idempotency of the semiring. Under this assumption, we study three problems: ontology completion with provenance, computing the set of relevant axioms for a consequence, and query answering.
Statistical Causality for Multivariate Non-Linear Time Series via Gaussian Processes

The ability to test for statistical causality in linear and non-linear contexts, in stationary or non-stationary settings and to identify whether statistical causality influences trend of volatility forms a piratically important class of problems to explore in multi-modal and multivariate processes.In this paper we develop novel testing frameworks for statistical causality in general classes of multi-variate non-linear time-series models. Our framework accommodates flexible features where causality may be present in either: trend, volatility or both structural components of the general multivariate Markov processes under study. In addition, we accommodate the added possibilities of flexible structural features such as long memory and persistence in the multivariate processes when applying our semi-parametric approach to causality detection.We design a calibration procedure and formal testing procedure to detect these relationships through classes of Gaussian process models. We provide a generic framework which can be applied to a wide range of problems, including partially observed generalised diffusions or general multivariate linear or non-linear time series models. We develop several illustrative examples of features that are easily testable under our framework to study the properties of the inference procedure developed including power of the test, sensitivity and robustness. We then illustrate our method on an interesting real data example from commodity modelling.
Artificial Intelligence Algorithms Selection and Tuning for Anomaly Detection

Czako, Zoltan Sebestyen, Gheorghe Hangan, AncaArtificial intelligence (AI) algorithms have recently become a topic of interest, among researchers and commercial product developers. A large number of AI algorithms aiming different kind of real-life problems have qualitatively different results, based on the nature of data, the nature of the problems and based on the context in which they are used. Selecting the most appropriate algorithm to solve a particular problem is not an easy task. In our research, we focus on developing methods and instruments for the selection and tuning of AI algorithms, to solve the problem of anomaly detection in datasets. The goal of our research is to create a platform, which can be used for data analysis. With this platform, the user could be able to quickly train, test and evaluate several artificial intelligence algorithms and also they will be able to find out which is the algorithm that performs best for a specific problem. Moreover, this platform will help developers to tune the parameters of the chosen algorithm in order to get better results on their problem, in a shorter amount of time.
Malaria Detection using Machine Learning

A life-threatening disease called Malaria is caused by parasites that get transmitted into people's body by the bites of infected mosquitoes. If Automation is used for the diagnosis process of this disease it will enable accurate diagnosis of the disease and hence it will deliver a reliable health-care to resource-scarce areas. Currently malaria is being diagnosed by performing microscopic tests on blood smears of the patients. They require examination by a skilled and well experienced laboratorian with a good vision. We are using Deep Learning to classify whether the given sample is infected or not. Staining method is a traditional method used for the identification of the infected cells. The Machine with our algorithm is trained with a set of data which consists of microscopic images of this thin blood smear of both infected and uninfected data, and then comparison and the classification accuracy is obtained by the Deep Neural Network obtained through training, validating and testing with various combinations of the dataset. Keywords: component, formatting, insert, machine learning, style, styling
PEMANFAATAN GOOGLE MAPS DALAM PEMBUATAN PETA KHUSUS UNTUK MENINGKATKAN ECOLITERACY PESERTA DIDIK

The background of this scientific writing is based on the low awareness of environmental conditions (eco-literacy) of the students, the limitations of local media and sources of learning, natural disasters, the use of ICT for negative things, and the urgency of mastering 21st-century skills. The research objectives through these Best Practices that: the students are expected to be able to make special-purpose maps and have environmental awareness (eco-literacy), able to formulate environmental problems and solutions to overcome these problems and the students have 21st-century skills. This scientific paper is in the form of Best Practice, which refers to Export Facto research because it has done by the author and obtained several results, namely: first, students are able to make a special map regarding the distribution of oil palm plantation and tin mining in the East Belitung Regency by utilizing digital map technology (google maps). Second, the students have eco-literacy. Third, the students are able to formulate solutions that can be done to deal with environmental problems in their area. Fourth, the students are trained to have 21st-century skills.
Clustering-based Optimization for Side Window Filtering

Side window filtering (SWF) is a new technique that significantly improves edge preserving capability. Based on our observation, it still suffers from edge blurring caused by filtering across edges. To address the issue, a clusteringbased optimization is proposed for SWF, which is motivated to only use the pixels that are not across edges in the filtering process. With the clustering strategy, the image is first grouped into perceptually homogeneous regions, so that the pixels on two sides of an edge are divided into different clusters. Each cluster is assigned with a unique label, and the pixels in the same cluster share the same label. In each side window, only the pixels that have the same label with the one being processed are used for filtering. Extensive analysis and experimental results show that the proposed optimization can further improve edge preserving capability as compared to SWF. Meanwhile, the increased complexity of the proposed optimization is only O(N), which is linear in the number of image pixels.
The Use of <i>Sistem Pembelajaran Daring</i> (SIPDA) as an Online Learning Management System on Learning Outcomes of Financial Accounting Subject: A Mediation Effect of Learning Motivation

This study aims to determine the effect of SIPDA on Learning Outcomes through Student Learning Motivation taking the Financial Accounting subject.This study population was 70 students in the Accounting Education Department of Faculty of Economics of Universitas Negeri Medan (UNIMED) and used as respondents.The questionnaire consisted of 18 items for SIPDA and 6 question items for learning motivation.This study applies the outer model test, the inner model test, the direct influence hypothesis test, and the indirect hypothesis test using the SmartPLS 3.0 program.The result of data analysis shows that: (1) There is a negative and significant influence between the use of SIPDA on the learning outcomes of accounting class 2019 students with a path coefficient of -0.371 and P-value <0.05, which is 0.025 and the value of T Statistics > 1.96 namely 2.243; (2) There is a positive and significant influence between the use of SIPDA on students' learning motivation for Accounting Education Class of 2019 with a path coefficient of 0.743 and a P-Value <0.05 which is equal to 0.000 and a value of T Statistic> 1.96 which is 13.568; (3) There is a positive and significant influence between learning motivation on student learning outcomes of Accounting Education Class of 2019 with a path coefficient of 0.373 and a P-Value <0.05 which is equal to 0.029 and a value of T Statistics> 1.96 which is 2.188.(4) There is a positive and significant influence between the use of SIPDA on learning outcomes through students' learning motivation for Accounting Education Class of 2019 with a value of 0.277 with a P-Values <0.05, which is equal to 0.045, and a Statistical T value> 1.96 which is 2.014.
THE EFFECT OF ROLE PLAY METHOD (RPM) ON STUDENTS’ ACHIEMENT IN SPEAKING

This study was aimed to find out the effect of using role play method (RPM) on the student’s achievement in speaking. This study was conducted by using an experimental design with two-groups. They were experimental and control groups. The location of the research is SMA Negeri 11 Medan. For efficiency and practicality of the research, 60 students were taken as the sample. The data of this research was collected by administrating a test. There was prepared speaking test through role play. To analyze the data, the writer used t-test. The result of the test shows that to 3.14 was higher than the value of ttable from table at the level significant 0f 0.05 (2.000). In other words, it can be said that t- observed or t0 was higher than ttable at the level of significant of 0.05 (2.002) at df= 58 for two tailed test. It can be concluded that the students’ achievement taught by using role play method (RPM) was higher than taught by memorizing dialogue in speaking.
Hybrid Computer Approach to Train a Machine Learning System

This book chapter describes a novel approach to training machine learning systems by means of a hybrid computer setup i.e. a digital computer tightly coupled with an analog computer. As an example a reinforcement learning system is trained to balance an inverted pendulum which is simulated on an analog computer, thus demonstrating a solution to the major challenge of adequately simulating the environment for reinforcement learning.
Patterns of Presentations In Mammography in a Developing Country

Introduction: Breast cancer is the second commonest cancer (7.2%)in Nepal and almost 54% patients present in the advanced stage.It is the leading cause of cancer death in females. The objective of the study was to determine the composition of breast density, BIRADS category and type of mammography. Methods: The study was conducted in a tertiary hospital from Jan 1st to Oct 30th of 2019. A total of 388 persons were included in the study. The mammographic findings were assessed by categories based on the BIRADS system. Results: Mammography for screening was 38 percent and diagnostic was 68 percent. Common breast compositions were of B and C. More frequent BIRADS categories were seen in 1 and 2. Conclusion: Dense breast is common in mammography. BIRADS categories 1 and 2 were more common than other categories.
Analisis kemampuan membaca pemahaman ditinjau dari minat belajar pada peserta didik kelas 5 sekolah dasar

&lt;em&gt;Reading become the most important aspect of Bahasa Indonesia learning because reading is some activity that implicate visual activity start from translating until understanding the poin of the text. The good comprehension reading capability is when someone competent to understanding the content of the text. There are some indicators to measure the comprehension reading capability. Comprehension reading capability should be implemented to elementary school studentespecially at grade V. Basic competency that demand the student to understand and analysis the text need to use the comprehension reading capability. Obstacle that happen frequently are students that have less interest about a text. This low interest to learn a text make them have a low comprehension reading capability. Research to Djama’atul Ichwan elementary school students at grade V represent that their comprehension reading capability are good enough seen from the high study interest. With thath high study interest and high effort will get the good comprehension reading capability.&lt;/em&gt;
Effect of Learning Media, Family Environment and Income Parent of Student Learning Motivation Smk 46 Jakarta

This study aims to: 1) the effect of learning media on student motivation; (2) determine the influence of family environment on student motivation; (3) the effect of parental income on the students motivation; and (4) the effect of instructional media, family environment, and income of parents of student motivation. The population in this study were all students of SMK 40 Jakarta administration office. To answer the hypothesis in this study using multiple regression with F test and t test. It can be concluded that: (1) there is no Instructional Media Influence on Student Motivation; (2) there is no influence of family environment on student motivation; (3) does not influence the income of parents of the students' motivation.
Pengaruh Hands On Activity Terhadap Prestasi Belajar Ekonomi Siswa Kelas Xi Ips Sma Negeri 1 Kartasura

The purpose of this study was to determine 1) the effect of hands on activity on learning intensity; 2) the effect of distance to school on learning intensity; 3) the effect of learning intensity on economic learning achievement; 4) the effect of hands on activity on student achievement both directly and through the intensity of learning; 5) the effect of distance to school on student economic learning achievement both directly and through the intensity of learning. The research population of class XI IPS SMA N 1 Kartasura, namely 175 students. Samples were taken by 89 students. Data collection methods documentation and questionnaire methods. The data analysis method uses simple regression analysis and path analysis. The results obtained by the regression equation Z = 0.645 X1 + (-0.101) X2 + e1 and Y = 0.678 X1 + (-0.075) X2 + 0.804 Z + e2. The regression equation shows 1) there is a significant positive effect between hands on activity on learning intensity seen from beta (β) 0,645 with a significant level of 0,000; 2) there is no positive and insignificant influence between distance to school on learning intensity as seen from the value of beta (β) -0.101 with a significant level of 0.344; 3) there is a significant positive influence between the intensity of learning on economic learning achievement seen from the beta value (β) 0.804 with a significant value of 0,000; 4) the path analysis results have an indirect effect between hands on activity on economic learning achievement through learning intensity 0.678 with a total effect of 0.678 + 0.518 = 1.196. 5) the results of the path analysis have no effect between distance to school on economic learning achievement through learning intensity -0,081 with a total effect of -0,075 + (-0,081) = -0,156.
Digital Video Watermarking: Features, Techniques, and Challenges

The advent of digital technology can createseveralidentical copies of the original multimedia assets comprise of image, audio and video objects. Yet, these capabilities introduce several uncover issues includes illegitimate redistribution, ownership protection, content authentication, fingerprinting, copyright and many more. Digital video watermarking is a potential emerging solution to protect the digital multimedia from such threads. The technique suggested to hiding the authenticated information intothemultimedia assets like image, audio or video in such manner that it doesn’t affect the quality of video objects and can be easily be extracted by certified user whenever required. On contrary, difficult to manipulate or remove by illicit person. The paperelaborates the features, applications, existing techniques, major categories of security attacks and related work done by previous researchers on digital video watermarking. Finally, the paper reviewed suchconcernwhich, still, requiredbeingdeterminedin digital video watermarking.
REAL-TIME MONITORING OF SHARED SPACES. INTERNET OF THINGS AS A MEAN TO MOTIVATE STUDENTS IN THE "FACULTAD DE INFORMÁTICA" OF THE NATIONAL UNIVERSITY OF LA PLATA (UNLP)

Information about the paper titled "REAL-TIME MONITORING OF SHARED SPACES. INTERNET OF THINGS AS A MEAN TO MOTIVATE STUDENTS IN THE "FACULTAD DE INFORMÁTICA" OF THE NATIONAL UNIVERSITY OF LA PLATA (UNLP)" at IATED Digital Library
On the Fluted Fragment (Invited Talk).

The fluted fragment is a recently rediscovered decidable fragment of first-order logic whose history is dating back to Quine and the sixties of the 20th century. The fragment is defined by fixing simultaneously the order in which variables occur in atomic formulas and the order of quantification of variables; no further restrictions concerning e.g. the number of variables, guardedness or usage of negation apply. In the talk we review some motivation and the history of the fragment, discuss the differences between the fluted fragment and other decidable fragments of first-order logic, present its basic model theoretic and algorithmic properties, and discuss recent work concerning limits of decidability of its extensions.