Reviewerly/exHarmony huggingface dataset:
SISTEM MONITORING PENJUALAN BAHAN BANGUNAN MENGGUNAKAN PENDEKATAN KEY PERFOMANCE INDICATOR

PT Indojava Kreasindo is one of the distributors of Kramik sanitari granite, which has a role in distributing these goods throughout the city of Lampung and its surroundings. The management of ceramic and granite sales data has been summarized in the sales recap sheet for each sales each month in writing so it takes a long time to find out the achievements of each sales each month. Therefore, the authors propose the application of sales monitoring to make it easier to find out the achievements of each sales per month. To solve this problem, we need the most effective solution, which is the Development of Monitoring Application for Building Materials Sales Using the Key Performance Indicator (KPI) Approach. In the development of this application using the waterfal method. For the data flow method using the Usacase Diagram, Activity Diagram. The concept used is monitoring using the KPI approach to facilitate the general manager in monitoring the sales of each sales. With the construction of a web-based information system and sales monitoring application aims to make it easier for the general manager to monitor the sales of each sales so that it does not require a long time and is presented in the form of a dashboard.
Analysis of Transactions 212 Mart Kuto Palembang to Find Frequent Patterns Among Itemset Using Association Rule Mining

Minimarket 212 Mart Palembang Kuto is a retail market of 212 mart cooperative cooperatives, selling various kinds of goods for daily needs ranging from food, non-food, drinks and frozen food.All transactions carried out in transactions, which are stored in datacenter warmart 212, consist of spreadsheets that start operating from February 21 th , 2019.Transaction data that is recorded and stored in warehouses are large and piling up, it is estimated that there are 58,068 transactions until February 21 th , 2019.Therefore, Data mining is used to create transaction data that is needed by getting information in the data using the rule association method with depth-first search or eclat search algorithm.Results obtained from the owners of 212 Palembang Kuto with the hope of increasing sales through bundling of goods, the right promotion and refilling of goods accordingly.
A Brief Didactic Theoretical Review on Convolutional Neural Networks, Deep Belief Networks and Stacked Auto-Encoders

This paper presents a brief theoretical review on deep neural network architectures, deep learning procedures, as well assome of its possible applications.The paper focuses on the most common networks structures: Convolutional Neural Network (CNN), Deep Belief Network (DBN) and Stacked Auto-encoders (SA).The building blocks which enabled the construction of deeper networkssuch as Rectified Linear Unit (ReLU) and softmax activation functions, convolution filters, restricted Boltzmann machines and autoencoders, are explained in the beginning and middle sections of the paper.A few examples of hybrid systems are also presented at the last sections of the paper.The paper concludes with some considerations on the state-of-art work and on the possible future applications enabled by deep neural networks.
Sistem Pakar Diagnosa Penyakit Avian Influenza Pada Bebek Menggunakan Metode Certainty Factor

Ducks are widely farmed poultry animals, because of its large population, the disease is also caused a lot. In avian influenza disease is an infectious animal disease caused by a virus and can infect humans. Avian invluenza is also a cause of decreased quality of meat and eggs, can even cause death in ducks. Disease in ducks is difficult to know because people have no prior experience. Communities and breeders find it difficult to take appropriate action on ducks affected by avian influenza so that they can be fatal. During 2012 - 2017 there were 1856 cases of bird flu in Indonesia. In October 2017 there were 3 cases of avian invluenza. Where 900 ducks died. From the explanation above, an expert system is needed to diagnose avian influenza. This system can help ordinary people and farmers who do not have experience in dealing with avian influenza. Disease data and symptoms of avian influenza that will be entered into this expert system can be obtained from interviews with experts and see references of knowledge about duck symptoms. So that this expert system can help in dealing with AI disease. In testing the validity, the calculation of the system manually and from expert diagnosis are the same. So that this application is feasible to use for the community and breeders.
Enhancing Students’ Literacy Skills through the use of English Storybooks

&lt;p&gt;Basically, education holds a huge influence on the outcome of the students in the process of learning. One obstacle in education today is concerning students’ literacy skill. Most of them possess a low interest in literacy. The strategy that can be adopted to enhance students’ literacy is through the use of storybooks. Numerous storybooks and various activities are provided in order to solve this difficulty, particularly in English education as a foreign language. The storybook can simplify the understanding of the foreign language. The stories are educational materials that provide effective learning with reality, recognition, and entertainment. The objective of this study is to enhance the literacy skills of students by using English storybooks, specifically for young learners and accommodate the widest opportunity for teachers to develop their English teaching abilities in such a way that in the future they are better prepared and alert in dealing with the identical situations.&lt;/p&gt;
A retrospective of the research on musical expression conducted at CEGeME

CEGeME - Center for Research on Musical Gesture and Expression is affiliated to the Graduate Program in Music of the Universidade Federal de Minas Gerais (UFMG), hosted by the School of Music, Belo Horizonte, Brazil, since 2008. Focused on the empirical investigation of music performance, research at CEGeME departs from musical content information extracted from audio signals and three-dimensional spatial position of musicians, recorded during a music performance. Our laboratories are properly equipped for the acquisition of such data. Aiming at establishing a musicological approach to different aspects of musical expressiveness, we investigate causal relations between the expressive intention of musicians and the way they manipulate the acoustic material and how they move while playing a piece of music. The methodology seeks support on knowledge such as computational modeling, statistical analysis, and digital signal processing, which adds to traditional musicology skills. The group has attracted study postulants from different specialties, such as Computer Science, Engineering, Physics, Phonoaudiology and Music Therapy, as well as collaborations from professional musicians instigated by specific inquiries on the performance on their instruments. This paper presents a brief retrospective of the different research projects conducted at CEGeME.
THE FRACTAL ANALYSIS OF SAMPLE AND DECISION TREE MODEL

Context. The problem of decision tree model synthesis using the fractal analysis is considered in the paper. The object of study is a decision trees. The subject of study is a methods of decision tree model synthesis and analysis.Objective. The objective of the paper is a creation of methods and fractal indicators allowing jointly solving the problem of decision tree model synthesis and the task of reducing the dimension of training data from a unified approach based on the principles of fractal analysis. Method. The fractal dimension for a decision tree based model is defined as for whole training sample as for specific classes. The method of the fractal dimension of a model based on a decision tree estimation taking into account model error is proposed. It allows to built model with an acceptable error value, but with optimized level of fractal dimensionality. This makes possibility to reduce decision tree model complexity and to make it mo interpretable. The set of indicators characterizing complexity of decision tree model is proposed. The set of indicators characterizing complexity of decision tree model is proposed. It contains complexity of node checking, complexity of node achieving, an average model complexity and worst tree model complexity of computations. On the basis of proposed set of indicators a complex criterion for model building is proposed. The indicators of the fractal dimension of the decision tree model error can be used to find and remove the non-informative features in the model.Results. The developed indicators and methods are implemented in software and studied at practical problem solving. As results of experimental study of proposed indicators the graphs of their dependences were obtained. They include graphs of dependencies of number of hyperblocks covering the sample in the features space from size of block side: for whole sample, for each class, for different set error values and obtained error values, for varied values of resulted number of features and instances, also as graphs of dependencies between average and worst tree complexities, decision tree fractal dimensionality and tree average complexity, joint criterion and indicator of feature set reduction, and between joint criterion and tree fractal dimensionality/Conclusions. The conducted experiments confirmed the operability of the proposed mathematical support and allow recommending it for use in practice for solving the problems of model building by the precedents.
A Study of Blockchain Solutions for IoT Issues

Through the years, the blockchain has procured too much traction from the core technology in place.Blockchain is used in various areas like medical record keeping, equity trading, supply chain, immutable data backup systems etc.The Internet of Things (IoT) deals with the interactions between the present day smart devices to procure data and take decisions usually without user intervention to automate tasks. With such a network of variety of sensors and interconnected electronics, the collection of information in our world can be achieved at a very detailed level. Such digital knowledge will help in delivering services that are advanced in a wide scope of application domains including smart city services and healthcare. This incrementally diverse collection, processing of data related to private lives of people giving rise to serious security and privacy issues. The problem with IoT devise is , lower amount of computing power, limited storage capacity and considerably low network bandwidth. This makes them very likely to get attacked and misused. This paper addresses crucial issues related to the security of IOT and explores ways in which integration of blockchain might solve them. The issues that have yet not been tackledpost the implementation of the blockchain have also been stated.
Analysis of Moodle Features to Build Moodle-Lite

Moodle is one of the LMS (Learning Management System) applications used to build E-Learning.Moodle is quite popular compared to other LMS because it provides several main advantages, namely free, feature rich and large community support.Even so, Moodle has some problems that are still being improved, like a userfriendly interface.This problem will be felt by ordinary users.In addition, the number of Moodle features both main features and additional features (plugins) causes Moodle's complexity to be even higher.In fact, sometimes users only use a number of features to help their work.Therefore, this study proposes a simple design of Moodle-Lite but more effective to the users.The basic foundation of Moodle-Lite design lies in two aspect, how often a feature is used and being talked by Moodle users.The first aspect is obtained by looking at the result in data survey from features that are often used.The second aspect is obtained through the analysis of a feature that is often discussed in Moodle Forum (MD).Furthermore, the best features obtained from both aspects will be made into Moodle-Lite features.The final results obtained six main features, namely: Quiz, Assignment, Feedback, Forum, Lesson, and Scorm.
Kontribusi Lingkungan Belajar Terhadap Kemampuan Literasi Teknologi Siswa di SMKN 7 dan 9 Padang

The problem in this research is the development of technology such as computers and the internet is not used for learning purposes but for things that are not important such as social media and games, the lack of habit of using technology such as computers to support the learning process results in the emergence of computer anxiety (doubt in using computers. from the doubt of using this computer, the motivation of students to use computers effectively will decrease and the learning environment does not support the sustainability of the learning process by utilizing technology to the maximum, then the lack of motivation of students in doing school work using existing technology and the lack of the role of teachers or parents in control the use of technology by students so that technological development is not utilized for the benefit of learning. The purpose of research is to know the learning environment that contributes to the ability n technology literacy of students. This research uses correlational quantitative research. Based on the results of the study it was found that there was a positive and significant contribution to the learning environment towards the ability of students' technology literacy with a contribution percentage of 64.5%.
Positional skipgrams for Bambara: a resource for corpus-based studies

This article presents a new online dataset of linguistically rich n‑gram frequency data for Bambara based on the disambiguated part of the Bambara Reference Corpus. The n‑grams in the dataset are positional skipgrams that capture information about co-occurrence of lexical items with grammatical categories at various relative positions. These n‑grams were constructed with the aim to leverage those types of information that are available in the morphologically annotated corpus of Bambara given the limited amount of textual data. The methodology and data used for constructing n‑grams for Bambara are discussed, followed by brief illustrations of how the positional skipgrams data may be employed in corpus-based linguistic research.
Deciphering complex text-based CAPTCHAs with deep learning

Background: CAPTCHA is a mechanism to distinguish humans from bots.It has become standard means of protection from the misuse of resources on World Wide Web.Different types of CAPTCHAs are implemented but text-based schemes are the most widely used due to its easiness and robustness.A user is asked to type in the text from an image.The image is intentionally distorted to dodge the bots.Recognizing the text is easy for humans but very hard for computers.Method/Findings: In this work, a text-based CAPTCHA scheme with background clutter and partially connected characters is decoded.The main steps consist on preprocessing, segmentation and recognition.Several digital image processing techniques were applied during preprocessing, segmentation steps and convolutional neural network (CNN) was used for recognition process.Since massive data is required for CNN therefore data was generated synthetically.A complex text-based CAPTCHA scheme with varying number of letters: 3, 4 and 5 letters is decoded with the overall precision of 77.5%, 64.2% and 51.9% respectively.
Alice: Arts Lab in Interfaces, Computers, and Everything Else - Research report (2019)

Located in the Computer Science Department of the Federal University of São João del-Rei, but not limited to just this space, ALICE has emerged as a research group focused on the development of software, art and technologies for the area of computer music and digital arts. Over the time, ALICE became the laboratory to develop technologies to an artistic group, called Orchidea, focused on the creation of digital art, encompassing students from diverse areas, such as Computer Science, Scenic Arts, Architecture and Music in a transdisciplinary context of art creation. In this way, this work aims to present the various initiatives and proposals carried out by the ALICE, addressing the development of technological products, through the tools implemented and the external tools used for teaching and for artistic creation. In addition, this work describes the current researches that are under development by the members of the groups, also highlighting the development of the digital performance titled “O Chaos das 5” and all the aspects and learning that we have obtained so far with this performance.
Exploration and Practice on Teaching Mode of General Chemistry Based on Smarter Education

摘要:
REFLECTIVE ABILITIES OF STUDENTS: WAYS AND MEANS OF DEVELOPMENT WHILE STUDYING GEOMETRY

A reflective ability as an ability to comprehend one’s experience, knowledge, evaluations is a psychological condition of thinking activity. But in researches, reflection is mainly considered as an indicator of a high level of thinking, creativity, the ability to analyze, types of reflection are not distinguished. For teaching mathematics, the development of intellectual reflection is especially important. In our study, the problem of the development of intellectual reflection is identified as an independent one. As a means of its development, we proposed geometric "many-valued" problems in which a situation of choice is organized. Three levels of development of reflective abilities in teaching geometry, in accordance with certain types of reflection are distinguished. The purpose of the study is to find out whether the level of development of students' reflection will change if "many-valued" problems are used in geometry. Is there a correlation of the manifestation of reflection on the geometric content and the content of another object? The experiment involved 375 students. The Pearson criterion was used in processing the results. The inclusion of "many-valued" problems in teaching geometry showed an increase in the level of formation of reflective skills of students and the transfer of these skills to another subject.
Automated Breast Lesion Segmentation from Ultrasound Images based on PPU-Net

Ultrasound is one of the most widely applied imaging modalities for breast lesion assessment. Breast lesion segmentation in ultrasound plays an important role in extracting features that is crucial for breast cancer diagnosis and treatment planning. Existing methods include manual or automated identifying breast lesion boundary. However, manual segmentation is time consuming and leads to inter- and intra-observer variations. Automated segmentation of breast lesion is highly required but very challenging due to the ultrasonic property of tissue. The neural network, an emerging automated segmentation technology, is widely used in medical images segmentation tasks and has achieved good performance. In this study, we proposed a novel pyramidal pooling U-Net network (PPU-Net) to segment breast lesion. In PPU-Net, the pyramid pooling module (PPM) was applied together with the U-Net to extract more scale information. The ratio of training set (408 cases) and test set (103 cases) was 4:1 in 511 breast lesion images. The PPU-Net approach was evaluated on the test set and achieved good performance. The evaluation metrics of Dice similarity coefficient (DSC) and accuracy (ACC) for PPU-Net were 88.97 ± 8.96% and 94.16 ± 4.02% respectively. For comparision, the DSC and the ACC based on U-Net and FCN were evaluated. The result demonstrated the feasibility that PPU-Net could be applied in breast lesion segmentation from ultrasound images and it has better performance than U-Net and FCN in this study.
Sensitivity analysis based artificial neural network approach for global solar radiation prediction in India

&lt;p&gt;The objective of this paper is to build an artificial neural network model to predict Global Solar Radiation (GSR) with improved accuracy using less number of best input parameters selected using sensitivity analysis. In this work, the input parameters used for training the artificial neural network (ANN) models are bright sunshine duration, maximum and minimum temperature, day length, months, extra terrestrial radiation (&lt;em&gt;H&lt;sub&gt;0&lt;/sub&gt;&lt;/em&gt;), relative humidity and geographical parameters of the locations namely the latitude and longitude. Sensitivity analysis is used to discover how the output data are influenced by the changeability of the input data.Three ANN models namely T-ANN, S-ANN and TS-ANN are proposed with most suitable input parameters selected using sensitivity analysis. The principle of this feature selection using sensitivity analysis is to improve the prediction accuracy of solar radiation models with less number of inputs. The proposed ANN model is also tested under noisy data and proved that ANN is able to perform reasonably good in GSR prediction on practical applications where the data is affected by noise caused by errors on measuring, fault of data acquisition system, recording problems, and so on.&lt;/p&gt;
Software Design of High Precision Filling Test System Based on LabWindows / CVI

Abstract Filling technology has a large number of applications in the chemical, food, pharmaceutical and other industries. Filling repeatability is an important indicator in the field of flow measurement. Because of its powerful data analysis capabilities, virtual instrument technology is playing an increasingly important role in the field of automated detection. This paper designs the host computer software based on LabWindows / CVI for a high-precision filling test system. LabWindows / CVI combines a powerful and flexible C language platform with professional measurement and control tools so it can realize the requirements for designing filling system software. The main functions of the software are hardware communication, data acquisition and processing, and export of test results then a user-friendly interface is designed. This article explains the basic program design process, data structure and introduces program functions. The software can run stably and can provide filling test solutions for different test devices.
STUDENTS’ DIFFICULTIES IN WRITING NARRATIVE TEXT

This research aims to describe students' difficulties in writing narrative texts, the students' more dominant problems and to know English teacher action to solve the problem. The researchers used qualitative research and a descriptive case study. This study tries to observe students' mistakes in writing narrative texts. The data collected based on the students' answer sheet. The categories made based on the students' answer sheets in writing Narrative texts. The things that recorded by the researchers for giving scores are the four generic structures: Orientation, Complication, Resolution, and Re-Orientation/Coda. And the researchers found that the students still had difficulties in writing narrative texts with the generic structures. Based on the findings, from 26 students only 3 students can distinguish between parts of generic structures and the rest must practice at home and ask the teacher to make improvements.