Reviewerly/exHarmony huggingface dataset:
Text analysis on health product reviews using r approach

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.
Improved Progressive Secret Sharing with Priority Weight

This paper proposes a new technique on the Progressive Visual Secret Sharing (PVSS) with priority weight. It overcomes a slight limitation of former PVSS scheme in terms of the lossless result on the recovered secret image. The former method yields a promising result while the total number of shared images is even, however, it produces imperfect recovered secret image if the total number of shared images is odd. The proposed method achieves the lossless result on the recovered secret image while the total number of shared images is odd or even. The experimental results demonstrate the effectiveness of the proposed method compared to former schemes in the progressive secret image reconstruction.
Strongly Secure Ramp Secret Sharing Schemes from Any Linear Secret Sharing Schemes

A secret sharing scheme (SSS) is a cryptographic tool to protect a secret from loss and leakage by dividing it into shares. A ramp SSS can improve the efficiency in terms of the sizes of shares by allowing partial information about the secret to leak out. In order to prevent the partial information from being recovered explicitly, the notion of the strong security has been introduced. However, there have been proposed few methods to construct strongly secure ramp SSSs for general access structures and they are not always sufficient. In this paper, we show that any linear ramp SSS can be transformed into a strongly secure scheme with the same access structure preserving the information ratio.
Efficient Tunable Microwave Single-photon Source Based on Transmon Qubit

Single-photon sources of high efficiency are of great interest because they are the key elements in many prospective quantum technologies and applications. Based on our previous work, here we demonstrate a high-quality tunable microwave single-photon source based on transmon qubit with intrinsic emission efficiency more than 99%. To further confirm the single-photon property of the source, we study the single-photon interference in a Hanbury-Brown-Twiss (HBT) type setup and measure the correlation functions of the emission field using linear detectors with GPU-enhanced signal processing technique. The antibunching in second-order correlation function is clearly observed. The theoretical calculations agree well with the experimental results. Such a high-quality single-photon source may be used as a building block for quantum communication, simulation and information processing in microwave regime.
Embedded System for Human Detection Applied to Domotics

This paper presents the development of an embedded system prototype that performs the home security monitoring, through image processing and classification algorithms to detect a human presence.If a human presence is detected, the system will send an alert message to the user.The embedded system is implemented on a Raspberry Pi 3 B, supported by a Pyroelectric Infrared Radial (PIR) motion sensor and a Raspberry Pi Camera V2.The algorithms are implemented in C language and were designed to take advantage of the hardware resources of the platform, through High Performance Computing (HPC) techniques.The selected classifier is a multilayer perceptron.This classifier obtained an accuracy of 96 %, approximately.
Difficulty of Summarization Items for Japanese Learners: Effects of Passages, Distractors, and Response Formats

This study aims to examine factors affecting the difficulty of summarization items for Japanese learners. In the process of item development, a connection between cognitive features related to target construct and the difficulty of test items is necessary to check empirically in order to define the abilities to be measured. Previous studies have mainly focused on local reading comprehension, while this study addressed summarization skills at the paragraph levels. The study originally developed items for the experiment to elicit three macrorules to represent a gist of the paragraph and text; deletion, generalization, and integration. And this study evaluated the influences that passages, distractor characteristics central to summarization processes, and response formats have on item difficulty, using the item difficulty modeling. When editing distractors, characteristics of L2 learners’ summarization were carefully reviewed and reflected. The participants included 150 freshmen from Japan, who were asked to answer experimental summarization items. The results of the linear logistic test model indicated that the main source of difficulty in summarization items was distractor characteristics. In particular, summaries with unnecessary information or lacking necessary information increased difficulty. In addition, summaries with detailed information, such as episodes and examples, and with a viewpoint that differed from the author’s also increased difficulty. The effect of passage differences was found to be minimal. Difference in response formats moderately affected item difficulty, and the extended-matching format was slightly difficult than the conventional multiple-choice format. This study suggested that test developers and item writers should care about the distractor development, to contain students’ errors when measuring summarization skills of L2 learners.
Cyber: Threats in social networking websites and physical system security

A social network may be a social system made up of people or organizations referred to as nodes, that are connected by one or additional specific kind of reciprocity, like friendly relationship, common interest, and exchange of finance, relationships of beliefs, information or status. A cyber threat will be each unintentional and intentional, targeted or non-targeted, and it will come back from a spread of sources, as well as foreign nations engaged in undercover work and knowledge warfare, criminals, hackers, virus writers, discontent staff and contractors operating inside a company. Social networking sites don't seem to be solely speaking or act with people globally, however conjointly one effective means for business promotion. In this paper, Tendency to investigate and study the cyber threats in social networking websites. the aim of this paper is to review and analyze these threats of social network and develop measures to shield the identity in cyberspace i.e., security of non-public data and identity in social networks are studied.
Getting Connected with Facebook Messenger: Exploring Meaningful Interactions through Online Chats in the ESL Context

The number of online users has unprecedentedly increased in recent years. The rapid advancement of technology has seen the growth of social media usage and this has made a huge impact on today’s educational system. However, to what extent has social media played an important role in the teaching and learning process particularly in the English Language Teaching (ELT) is still unclear. This is due to the fact that in most cases, social media is used for the purpose of entertainment and personal usage. The current study attempted to explore the nature of communications via social media and how they can be used in improving students’ language skills. Participants in this study consisted of fifteen undergraduate students who were into their second year of the TESL program. They were involved in completing a grammatical task through the use of Facebook Messenger, an online platform where they engaged in chat activities. The discussion which was moderated by the researcher lasted 45 minutes for each session and five sessions were conducted where sentence combining activities were also done using the Facebook Messenger. Results obtained from the discourse analysis done which examined the participants’ accounts of their experiences, as well as the pedagogical features of the online platform, clearly show that this platform can be used as a pedagogical tool in improving language skills.
Target Recognition and 3D Pose Estimation Based on Prior Knowledge and Convolutional Neural Network for Robots

In the competition of RoboMaster, the robot needs to trigger the target, the “Energy Mechanism” which consists of nine different dynamic flame numbers, in a nine square area by shooting projectiles. Therefore, 3D target detection should be implemented including target recognition and 3D pose estimation in real-time. As the targets are dynamic flame numbers and quite small in the whole image, it increases the difficulty to detect. The robot should achieve to shoot the target in multi-angle and multi-scale to adjust the competition. To address these issues, we propose a fast and accurate method to detect all nine numbers and estimate each 3D pose based on prior knowledge and convolutional neural network only by a monocular camera. The geometric constraints around the target are employed as prior knowledge when estimating the target pose. Then, we utilize the relative position information to detect the region of each dynamic number in the image, which is recognized by a convolutional neural network trained by flame numbers. Experiments in the actual environment show that our method can achieve the detection of each dynamic number in real-time and high accuracy. The runtime is 29ms on average (about 11ms in detection and 18ms in recognition) and the recognition accuracy is about 94.69%. And our method wins the first place in the technical challenge of 2018 RoboMaster competition.
Chroma subsampling with modifications on luminance and chrominance values

For existing methods on YUV420 subsampling from YUV444 image format, the Y is constrained to change the UV values, or the UVs are constrained to change the Y value. We proposed a method of chroma subsampling with changing YUV values at the same time for YUV420 images. We proposed an assumption (observation) to reduce the algorithm complexity. The experimental results show that the proposed work is better than existing state-of-the-art methods by 1.64 dB and 0.61 dB on average in CPSNR (Composite Peak Signal-to-noise Ratio).
Abstract: Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences.
A Novel Bayesian Filtering Method for Systems with Quantized Output Data

In this paper we develop a novel scheme for state estimation of discrete-time linear time-invariant systems with quantized output data. We take a Bayesian approach, therefore, we describe the behavior of the a posteriori probability density function of the state. The difficulty of this problem lies in the probability function of the measurable output given the state, which we approach through an approximation by a Gaussian sum, that naturally leads to a Gaussian sum for the a posteriori density function.
Conocimiento y frecuencia del uso de las tecnologías de información y comunicación en la práctica educativa. Variables sociodemográficas de los docentes en la Universidad de Los Andes

This study was aimed at determining the relation between socio-ethnographic features of academics of two faculties at the University of the Andes in Venezuela, and the knowledge of Information and Communication Technology (ICT) and how frequent this is used. It is a descriptive-relational study for which a questionnaire measuring academics’ ICT skills was designed. The results show that academics trained in scientific and technical areas know ICT more and use it more frequently than the ones trained in social sciences. Additionally, length of service and promotion ladder heavily influence this: academics, who are at the bottom of the promotion ladder and whose service is not long, are more likely to know ICT more and use it more frequently in teaching.
Image Regularization with Morphological Gradient Priors Using Optimal Structuring Elements for Each Pixel

As an image prior for image restoration, the use of the sum of the morphological gradient for an image has been proposed. In this paper, we propose a method of optimizing the structuring element (SE) for each pixel, in particular, by greatly reducing the computational burden for optimization by adopting a new evaluation method for the SE in simulated annealing. By optimizing the SE for each pixel, edges of the image can be faithfully evaluated, and the improvement of restoration accuracy can be expected. An experimental result shows the effectiveness of the proposed method.
Research on Establishing Vehicle Driving Path in ADAMS Software by Ant Colony Algorithm

ADAMS software is the one of the most influential software in the field of virtual prototype simulation analysis, design and development, and has been widely used in the field of automotive. To solve the problem that the pavement model cannot be established in ADAMS due to the inability of field measurement and the lack of drawings, this paper studies a method which can extract effective information only from the pictures. Firstly, the acquired pictures are preprocessed, and by means of graying, binarization, gaussian transformation and other processing means, the storage space and computing speed occupied by irrelevant data are reduced while the expression ability of the images is preserved. Then we do a directed sorting of the previously extracted pixel coordinates, this problem approximates a large-scale TSP problem. Finally, the results are imported into ADAMS for final simulation, which verifies the effectiveness of the method described in this paper.
PENGARUH MOTIVASI DAN SARANA SEKOLAH TERHADAP HASIL BELAJAR SISWA DI KELAS IV SEKOLAH DASAR

ABSTRACT This research as to know how great the influence, the motivation, infrastructures and children learning to study results especially primary school class IV. Subjects used are the fourth grade students in Elementary School Inpres Oekaem, Meusin Village, Boking Sub-District, South Central Timor District, East Nusa Tenggara Province. While the study sample was 40 students consisting of 2 classes namely class IV-A total of 20 students and class IV-B amounted to 20 students. The model of data analysis used in this study is multiple linear regression analysis. Based on the results of research can be concluded that the variable motivation and learning facilities and infrastructure affect the educational outcomes of children's learning . Keywords: Motivation, Facilities and Infrastructure Learning, Learning Outcome s. ABSTRAK                                                                                          Penelitian ini bertujuan untuk mengetahui seberapa besar pengaruh motivasi belajar, dan sarana prasarana belajar terhadap hasil belajar anak kelas IV SD Inpres Oekaem. Subjek yang digunakan adalah siswa kelas IV di SD Inpres Oekaem, Desa Meusin, Kecamatan Boking, Kabupaten Timor Tengah Selatan-Provinsi Nusa Tenggara Timur. Sedangkan sampel penelitian adalah 40 siswa yang yang terdiri dari 2 kelas yakni kelas IV-A berjumlah 20 siswa dan kelas IV-B berjumlah 20 siswa. Adapun model analisis data yang digunakan pada penelitian ini adalah analisis regresi linier berganda. Berdasarkan hasil penelitian dapat disimpulkan bahwa variabel Motivasi serta Sarana dan prasarana belajar berpengaruh terhadap pendidikan hasil belajar anak. Kata Kunci: Motivasi Belajar, Sarana dan Prasarana Belajar, dan Hasil Belajar Anak.
DEVELOPMENT OF MEDIA TRAINER KIT CHARGING SYSTEM TO IMPROVE EFFECTIVENESS AUTOMOTIVE ELECTRICAL PRACTICES

The research aims to find out; (1) The process of developing a media trainer kit charging system; (2) The feasibility of the developed media trainer kit charging system; and (3) The effectiveness of the developed media trainer kit charging system. This type of research is a research development using the 4-D model. The research subjects consisted of 20 students, while the object of research was the media trainer kit charging system. Data collection uses observation, questionnaires, and tests. Data were analyzed with descriptive (qualitative and quantitative) and inferential gain test statistics. The results showed that: (1) The process of developing a media trainer kit charging system with a 4-D includes 4 stages, namely define, design, develop, and disseminate; (2) The feasibility test of the media trainer kit charging system by the material expert is 82.5% categorized very good; by the media expert is 81.67% categorized very good; by course lecturer is 72.5% categorized good; by limited trial is 81.25% categorized very good; and extensive trial is 84.62% categorized very good; and (3) The results of the effectiveness test showed that the media trainer kit charging system developed was effectively used in lectures. Keywords: media trainer kit, charging system, automotive electrical
Pengaruh Model Pembelajaran Contextual Teaching and Learning (CTL) Terhadap Hasil Belajar Siswa Kelas V Pada Tema 9 Subtema 1 di MI YPSM Al Manaar

Abstrak Penelitian ini bertujuan untuk mengetahui perbedaan nilai rata-rata siswa sebelum dan sesudah menggunakan model pembelajaran Contextual Teaching and Learning (CTL) , dan untuk mengetahui perbedaan hasil belajar siswa antara yang menggunakan model pembelajaran CTL dengan yang tidak menggunakan model pembelajaran CTL. Penelitian ini menggunakan quasy experimental design dengan model nonequivalent control group. Subjek penelitian ini adalah seluruh siswa kelas V MI YPSM Al Manaar . Instrumen yang digunakan adalah soal tes dan lembar observasi. Hasil penelitian menunjukkan bahwa rata-rata nilai pretest kelas eksperimen sebesar 71,44 dan kelas kontrol sebesar 75,79. Setelah diberi perlakuan, rata-rata nilai kelas eksperimen sebesar 83,22 dan kelas kontrol sebesar 82,42. Hasil penelitian menyimpulkan bahwa ada perbedaan nilai rata-rata siswa sebelum dan sesudah menggunakan model pembelajaran CTL . Abstract This study aims to determine differences in the average value of students before and after using the Contextual Teaching and Learning (CTL) model, and to find out the differences in student learning outcomes between those who use the CTL model and those who do not use the CTL model. This research uses quasy experimental design with a nonequivalent control group model. The subjects of this study were all fifth grade MI YPSM Al Manaar students. The instruments used were test questions and observation sheets. The results showed that the average pretest value of the experimental class was 71.44 and the control class was 75.79. After being given treatment, the average value of the experimental class was 83.22 and the control class was 82.42. The results of the study concluded that there were differences in the average scores of students before and after using the CTL model.
The improved dehazing method fusion-based

This paper proposes a new way to clear up the fog which softly combines dark channel with color attenuation, namely DCP and CAP. The main task is to solve the bad performance in the sky and white bright regions based on the DCP and the problem of incomplete defogging near the object of the foreground based on the CAP. A quad-tree search method is applied to look for the atmospheric light A. We can choose the pixel average of the last image block as A to solve the problem of excessive saturation. The guided filter is applied to adjust the final medium transmission. By experiments, the results can prove that the proposed method is effective.
A Tool Detects Violation Of Road Markings Using Ultrasonic Sensors Based On Internet Of Things

In our daily lives, of course we often see the number of motorbike or car vehicles that do not care about the traffic lights, even though the traffic is red, but there are always vehicles that stop or even slightly cross the white line that is specifically for pedestrians so that can pass when the traffic lights are red. Ultrasonic sensors work based on the principle of reflection of sound waves, where these sensors produce sound waves which then capture it again with a time difference as the basis for sensing. After analyzing the needs, the author will build a Tool to Detect Road Marking Violations Using the Internet-Based Ultrasonic Sensor on the grounds that all the components needed in building the tool already exist. Which is where the driver will be exposed to a photo if it crosses the marking line that has been provided on the highway. Because the concept of the internet of things involves tools with users to communicate. The composition of the algorithm, the author uses python which has become the default programming language raspbian operating system. Algortihm construction in the tool is a red light algorithm, an ultrasonic sensor algorithm, an image capture algorithm, and an algorithm for sending text and images via email. Traffic officers will be facilitated because the tool is in accordance with the traffic light seconds, can take pictures, and also officers are informed in the form of text and images.
Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation

Product is the one of thing which more important in the business especially for the retail industry. Ramayana is the one exact place for selling retail products such as clothing, shoes, or slipper. On 2012-2018, the number of sales of products in Ramayana experience curve up and down. That thing can cause profit and lose for Ramayana, to avoid that thing need to be held a prediction for the next months so that Ramayana side can know what will happen in the next months in selling it’s product and can take a step for more effective in selling it’s products. The data which used in this research is the data report monthly product sales of shoes &amp; sandal sourced from Ramayana from 2012 until 2018. This research uses the Backpropagation neural network method using 5 architectures namely 3-26-1, 3-31-1, 3-35-1, 3-39-1 and 3-40-1. The best architecture is 3-35-1 with an accuracy rate of 92%. The results obtained are the results of the prediction of the number of sales for 2019, 2020, 2021 and 2022
Scale Invariance in Projection Selection Using Binary Tomography

In this paper, we propose two strategies of reducing the amount of data needed for binary tomographic reconstructions. We study how the direction dependency changes by reducing the resolution of an image and we point out how to specify the most informative angles for the original image using its do wnscaled version. We also show how to predict the final acceptable resolution. Applications of the proposed strategies are also mentioned.