Reviewerly/exHarmony huggingface dataset:
Swarm Intelligence for Additive White Gaussian Noise Level Estimation

This paper presents a simple technique for estimating the noise levels in noisy images corrupted by additive white Gaussian noise.The proposed technique modifies the existing singular-valuedecomposition-based noise level estimation method.The proposed method calculates the sum of trailing singular values to infer noise levels.Particle swarm optimization and its variants can be used compute the optimal scalar value for the proposed noise level estimation method over a set of training images.As discussed in the experimental section, the proposed method outperforms existing schemes on noise level estimation tasks.Additionally, the estimated noise obtained from the proposed method can be used to improve the quality of denoised images.
ANALYSIS OF RETRIAL FUZZY QUEUE WITH SINGLE SERVER SUBJECT TO BREAKDOWNS AND REPAIRS BY FLEXIBLE ALPHA-CUTS METHOD

This paper introduces the performance measures computation of a single server fuzzy retrial queue with breakdowns and repairs by a new technique called flexible cuts -α method.This one uses only one fuzzy arithmetic instead of two arithmetics used by mathematical nonlinear programming method.Basing on this approach, some characteristics as expected customers number in the queue, in the orbit or in the entire system can be computed successfully, and results are the same than those obtained by other methods.
Exploratory Data Analysis and ETL with SAS on Hadoop Eco-system with Cervical Cancer Dataset

Objective:The main objective of this project is to explore and analyse a secondary dataset which collected from "Hospital Universitario de Caracas" in Caracas, Venezuela. Methods:The dataset comprises 858 patients' information relating to demographic information and medical history data.There is a large number of records which are left with blank, which might be intentionally avoided by the patient due to privacy considerations.SAS Studio is utilized in data exploration and data pre-processing.Data cleaning and data transformation are conducted basing on the knowledge gathered in the process of data exploration.Afterwards, the dataset was exported from SAS Studio and uploaded to Hadoop Hortonworks platform for analysing purpose.Lastly, five hypotheses have been explored with the visualization tool of Tableau.
Design and implementation of a Web application for Cultural Heritage

Within the field of Digital Humanities, a great effort has been made to digitize documents and collections in order to build catalogs and exhibitions on the Web. In this paper, we present WeME, a Web application for building a knowledge base, which can be used to describe digital documents. WeME can be used by different categories of users: archivists/librarians and scholars. WeME extracts information from some well-known Linked Data nodes, i.e. DBpedia and GeoNames, as well as traditional Web sources, i.e. VIAF. As a use case of WeME, we describe the knowledge base related to the Christopher Clavius’s corre spondence. Clavius was a mathematician and an astronomer of the XVI Century. He wrote more than 300 letters, most of which are owned by the Historical Archives of the Pontifical Gregorian University (APUG) in Rome. The built knowledge base contains 139 links to DBpedia, 83 links to GeoNames and 129 links to VIAF. In order to test the usability of WeME, we invited 26 users to test the application.
Dynamic Clustering Personalization for Recommending Long Tail Items

Recommendation strategies are used in several contexts in order to bring potential users closer to products with a strong probability of interest.When recomendations focus on niche items, they are called recommendations in the long tail.In these cases, they also look for less popular items and try to find your target custumer, niche market.This paper proposes a long tail recommendation approach that prioritizes relevance, diversity and popularity of recommended items.For that, a hybrid approach based on two techniques are used.The first is clustering with dynamic parameters that adapt from according to the dataset used and the second is a type of Markov chains for to calculate the distance of interest of a user to an item of relevance for this user.The results show that the techniques used have a better relevance indexes at the same time more diverse and less popular recommendations.
Synthetic data usage for document segmentation models fine-tuning

In this paper, we propose an approach to the document images segmentation in a case of limited set of real data for training. The main idea of our approach is to use artificially created data for training and post-processing. The domain of the paper is PDF documents, such as scanned contracts, commercial proposals and technical specifications without a text layer is considered as data. As part of the task of automatic document analysis, we solve the problem of segmentation of DLA documents (Document Layout Analysis). In the paper we train the known high-level FasterRCNN \cite{ren2015faster} model to segment text blocks, tables, stamps and captions on images of the domain. The aim of the paper is to generate synthetic data similar to real data of the domain. It is necessary because the model needs a large dataset for training and the high labor intensity of their preparation. In the paper, we describe the post-processing stage to eliminate artifacts that are obtained as a result of the segmentation. We tested and compared the quality of a model trained on different datasets (with / without synthetic data, small / large set of real data, with / without post-processing stage). As a result, we show that the generation of synthetic data and the use of post-processing increase the quality of the model with a small real training data.
SISTEM INFORMASI PEMBAYARAN SPP PADA SMK SWASTA TELADAN TANAH JAWA MENGGUNAKAN VB.NET

The purpose of this study was to design a tuition payment accounting information system using Vb.Net which aims to simplify and accelerate the Teladan Tanah Jawa private vocational school in processing school fee payment data. The research method used in this research is observation, interview, sampling, and literature study. The analysis tools used are Data Flow Diagrams (DFD), Entity Relationship Diagrams and the relationships between tables. This information system makes it easier for the school administration to find student data, process arrears data, data on daily school fees and reports on tuition payments.
Fuzzy Cognitive Maps-Based Switched-Mode Power Supply Design Assistant System

Traditional engineering design approaches primarily solve technical problems and often ignore the importance of human factors. To reduce human errors and workload in power electronics, this paper proposes a switched-mode power supply design (SMPS) assistant system based on Fuzzy Cognitive Maps (FCMs). This system incorporates both technical requirements and human factors that involve designers' knowledge and skills in the SMPS design domain. First, we identify the critical concepts from power management lab kits and power electronics books, and extract latent sub-skills of SMPS design using exploratory factor analysis to build the starting concept list of FCM. Second, we use factor analysis and correlation analysis to determine the causal weights between the captured components to build the initial FCM based on the starting concept list of FCM. Third, through interviews with subject-matter experts, we get their inputs on the initial main map and capture their individual FCMs. Then, we integrate experts' individual FCMs with different weights. After that, we determine the degree of fuzzification of the threshold function through analyzing data collected based on the prediction results of the only decision concept in the proposed FCM - SMPS quality. Two WHAT-IF scenarios are analyzed based on different inputs using the FCM Expert tool. The scenario test results provide guidelines to designers in terms of knowledge or skills improvements and power supply debugging. Finally, we evaluate the proposed system using eight scenarios. The evaluation results of components' actual states are consistent with their preferred states, which suggests that the proposed FCM-based assistant system is reliable and effective. The proposed system provides useful guidelines in terms of knowledge or skills improvements for SMPS designers and can help improve the power supply design process.
Exploring and Comparing Unsupervised Clustering Algorithms

One of the most widely used approaches to explore and understand non-random structure in data in a largely assumption-free manner is clustering. In this paper, we detail two original Shiny apps written in R, openly developed at Github, and archived at Zenodo, for exploring and comparing major unsupervised algorithms for clustering applications: k-means and Gaussian mixture models via Expectation-Maximization. The first app leverages simulated data and the second uses Fisher’s Iris data set to visually and numerically compare the clustering algorithms using data familiar to many applied researchers. In addition to being valuable tools for comparing these clustering techniques, the open source architecture of our Shiny apps allows for wide engagement and extension by the broader open science community, such as including different data sets and algorithms.
A Standardized Bare Bones Particle Swarm Optimization Algorithm for Traveling Salesman Problem

The traveling salesman problem (TSP) plays an important role in theoretical computer science.It can be used to solve different route planning problems in the real world and has been proved to be NP-hard.Plenty of researchers tried to solve the TSP by the population-based algorithms.However, as a real-world problem, the TSP has two major differences with the benchmark functions.One is the visiting order of the TSP is a combination of integers and the other one is each city in the problem has to be exactly visited once.To cross the two problems, the standardized bare bones particle swarm optimization (SBBPSO) algorithm is proposed in this work.The Gaussian distribution is used to select the positions of particles in the next generation.A standardized converter is used to ensure the dimensions of each particle are integers and each city has been visited exactly once.To test the performance of the SBBPSO, several famous instances are used in the experiments.Also, the standard bare bones particle swarm optimizations algorithm is used as the control group.The experimental results confirm that the SBBPSO is able to solve the TSP.
Pengembangan Multimedia Interaktif Menggunakan Aplikasi Ispring Suite 9 Pada Pembelajaran IPA Kelas IX Di SMP Negeri 5 Panyabungan

Learning support facilities in SMP 5 Panyabungan are sufficient for the learning process of students, but innovation has not been maximized that utilizes technology for the learning process.This study aims to determine the development and feasibility of interactive multimedia using the Ispring suite 9 application on learning outcomes in natural science subjects in the form of learning using a computer or laptop that can be operated offline.This type of research is a Research and Development (R&D) using the ADDIE development model.Assessment of media experts, material experts, linguists, students and teachers is used as research data and analyzed with descriptive percentages.Based on this research, a product with a material expert judgment of 96% was obtained, media expert was 88%, linguist was 82.5%, student responses were 90%; and teacher responses by 95%.Based on these results it can be concluded that interactive multimedia using the ispring suite 9 application on natural science subjects is suitable as a learning medium for students of grade IX junior high school.
MAPPING JUNIOR HIGH SCHOOL STUDENTS’ FUNCTIONAL LITERACY COMPETENCE

Literacy issues have probably been a special concern of many countries. In Indonesia, there have also been several surveys regarding the literacy of school students. This study aims to map the functional literacy of junior high school (JHS) students by focusing on various types of literacy and how each of them contributes to the overall functional literacy. This is quantitative survey research whose subjects were JHS students in four regencies and one municipality of the Special Region of Yogyakarta, with a total sample member of 246 students. The research instrument was an extension of the National Assessment for Adult Literacy (NAAL) with some additions of functional literacy types suggested in the FGD. Data were collected by using a test and analyzed through descriptive statistics and confirmatory factor analysis using the structural equation model with the Lisrel program. The results reveal that the functional literacy of the JHS students is mostly in the “medium” (83.3%), “low” (15.4%), and “high” (1.2%) category. Of the 15 types of literacy, two of them (13.33%), namely prose and cultural literacy make “high” contribution to the functional literacy as a whole, and two others (document and environmental literacy) also support to a “medium” extent (13.33%), while the rest (60%) are categorized as having “low” contribution. This likely implies the importance of fostering various types of functional literacy at school for students to have the ability to develop their personal and social functions.
Learning Semantic Image Attributes Using Image Recognition and Knowledge Graph Embeddings

Extracting structured knowledge from texts has traditionally been used for knowledge base generation.However, other sources of information, such as images can be leveraged into this process to build more complete and richer knowledge bases.Structured semantic representation of the content of an image and knowledge graph embeddings can provide a unique representation of semantic relationships between image entities.Linking known entities in knowledge graphs and learning open-world images using language models has attracted lots of interest over the years.In this paper, we propose a shared learning approach to learn semantic attributes of images by combining a knowledge graph embedding model with the recognized attributes of images.The proposed model premises to help us understand the semantic relationship between the entities of an image and implicitly provide a link for the extracted entities through a knowledge graph embedding model.Under the limitation of using a custom user-defined knowledge base with limited data, the proposed model presents significant accuracy and provides a new alternative to the earlier approaches.The proposed approach is a step towards bridging the gap between frameworks which learn from large amounts of data and frameworks which use a limited set of predicates to infer new knowledge.
Meta analisis Pengaruh Model Pembelajaran Kooperatif Tipe Scramble Terhadap Hasil Belajar Siswa di Sekolah Dasar

This study aim ed to re-analyze the effect of scramble learning model to improve student s’ learning outcomes at elementary schools. Scramble learning model refers to a game of arranging the scrambled words and letters in order to form a certain meaningful word . Scramble learning can train students to be creative to arrang e random words and sentences in order. This study utilized a meta-analysis method. At t he first stage, the researcher formulate d the problem, then , sought the existing and relevant research to be analyzed. The technique of data collection in this study was a non-test, namely the documentary analysis method , by searching for electronic journals on Google Scholar with the criteria of comparative journals or articles. There were 11 articles that fulfilled the criteria for th is research . The results of the journal analysis indicated that scramble learning model c ould improve the students' cognitive learning outcomes . This was proved by the average difference of the results of the pre-test and post-test which was 24.9 . To strengthen the results , thes e data were tested by IBM SPSS statistical program 16 for windows with a paired sample T-test . Based on the result of this test , it was revealed that H 1 wa s accepted which implied that the scramble learning model ha d an effect on student s’ learning outcomes with the value of sig 0.00 &lt;0.05.
Face recognition using fractional coefficients and discrete cosine transform tool

Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.
Correlation of Reading Interests and Learning Motivation Toward Science Learning Outcomes

The low reading interests and learning motivation are the main problems in this study. That is due to the lack and support of families, schools, and communities to increase reading interests and learning motivation. This study aimed to determine the relationship of reading interests and learning motivation with the learning outcomes of science class V of primary school (elementary school). This type of research was ex-post facto research. The population of this study was 187 people. The sample was determined using random sampling techniques and 144 people were selected. The data collected was an interest in reading and student motivation to use questionnaire instruments and document recording. Data were analyzed by simple regression analysis, product-moment correlation, and multiple regression analysis. The results of the study obtained first, the value of r count 0.832&gt; r table 0.138 which means that there is a significant relationship between reading interests and science learning outcomes. Second, the value of r count 0.988&gt; r table 0.138 which means there is a significant relationship between learning motivation and science learning outcomes. Third, the value of r count 0.988&gt; r table 0.164 which means that there is a significant relationship between reading interests and learning motivation with the results of learning science. The relationship shows that if students' reading interests and learning motivation is high, student learning outcomes tend to be high. It means that students' reading interests and learning motivation are very important to be instilled in the learning process to maintain good learning outcomes and improve student learning outcomes.
THE IMPACT OF PBWORKS APPLICATION ON VOCATIONAL STUDENTS’ COLLABORATIVE WRITING SKILL

Writing skill is a productive language skills that should be mastered by vocational students. It has become a critical issue in the study of English for Specific Purposes (ESP) and a challenging one in Indonesian context in the context of English as Foreign Language learning. This paper aims to investigate the impact of PBworks media application on vocational students’ collaborative writing skill. The research used the experimental approach. The respondents included 60 vocational students selected randomly and divided into two groups with 30 students in the control group and 30 students in the experimental group. The PBworks media was applied in the experimental group, while the control group received face-to-face writing class without incorporating learning media. The data were obtained by comparing student scores in the pre-test and two post-tests. The analysis of covariance (ANCOVA) was used to obtain the result. It showed that there is a statistically significant difference between using PBworks and non-Pbworks media in regard to the students’ writing performance (p &lt; .05). Moreover, vocational students believe in the benefit of the PBworks media application in developing collaborative writing skill despite the disadvantages.
Sequent Calculi and Interpolation for Non-Normal Modal and Deontic Logics

G3-style sequent calculi for the logics in the cube of non-normal modal logics and for their deontic extensions are studied. For each calculus we prove that weakening and contraction are height-preserving admissible, and we give a syntactic proof of the admissibility of cut. This implies that the subformula property holds and that derivability can be decided by a terminating proof search whose complexity is in Pspace. These calculi are shown to be equivalent to the axiomatic ones and, therefore, they are sound and complete with respect to neighbourhood semantics. Finally, a Maehara-style proof of Craig’s interpolation theorem for most of the logics considered is given.
Analysis of Habit Patterns in Academic Behavior in Student Learning Discussions

The discussion method can encourage inactive students to be active and confident, but there are still some students who are not able to cooperate well. This study aimed to analyze the pattern of academic behavior habits in the learning discussion of grade 4 students. The population in this study was all grade 4 students at SD 5 Karangbener totaling 24 students including 15 girls and 9 boys. This research used data collection techniques including the stages of observation, interviews and field notes. This study used descriptive qualitative data analysis method. The results of the analysis of students 'academic behavior during the learning discussion were the classification of student behavior during discussion including praying before the lesson began, paying attention to the teacher's explanation, making material summaries, being on time in doing assignments, participating in discussions, daring to express opinions, respecting other students' opinions, writing the results of the discussion, conveying the results of the discussion confidently, reading diligently, enthusiastically taking part in learning, concentration during the learning process, diligently working on the exercises, daring to ask questions while having difficulty doing the exercises. The academic behavior of fourth grade students of SD 5 Karangbener was included in very good interpretations with behavioral indicators that have been found based on field data. The discussion method has a positive academic impact on students which can be viewed from the cognitive, affective, and psychomotor domains.