Reviewerly/exHarmony huggingface dataset:
Ekstraksi dan Analisis Produk di Marketplace Secara Otomatis dengan Memanfaatkan Teknologi Web Crawling

Along with the advancement in technology, todays community begins to abandon conventional shopping methods where buyers must come to the seller's shop. Nowadays community mostly doing online shopping because the process is considered more convenience. Because of this, there are more and more online marketplace users. Much more data can be retrieved with the increasing number of online marketplace users. Because of the large amount of data the process for extracting the data so that it can be seen and utilized becomes possible. The purpose of this journal is to show data and extraction method from an online marketplace system so that the results can be visualized and users can analyze the data. The data extraction method that will be used is the web crawling method and web scraping where after the data is successfully extracted and cleaned it will be visualized with the power BI application. The experiments show that the method is useful to conduct analysis.
Emotional Analysis of E-Commerce Online Comment Data

Introducing text emotion analysis into e-commerce online comment data can judge the emotional tendency of e-commerce online comments.At the same time, the application of Python Jieba participle and LDA topic model enables more detailed understanding of the emotional changes in online comments of e-commerce.Taking product reviews of Midea water heater on JD.com as an example, the empirical analysis shows that the online product review, by introducing text sentiment analysis, determines the consumer emotion tendency, which can help with the electric business improvement project for merchants to provide accurate marketing, set up new businesses evaluation way, dynamically monitor customer emotional tendency, and timely grasp the emotional trend of E-commerce industry.
Emotion Mining Mechanism over Texts in Social Media

Nowadays societies are clearly bound to social networking through the Internet.It is common to find out people posting remarks, quotes or moods in social media.Leaders, politicians, celebrities, and ordinary people have posted on social media as a regular in their expression range.Even organizations generate postings on social networks.Humans' manifestation is always linked to the emotions they have.Authors believe that it is possible to discover the sentiment expression from texting.This proposal consists of a model to gather, classify, and emotionally-assess the texting in social networks.The model collects text posts from social media, processes the postings, and generates an emotional assessment.Such evaluation could be bound to the mood of postings' authors.The emotional-assessment consist of inference mechanisms based on knowledge coming from affective dictionaries and automated reasoning.
Utilization of Information and Communication Technology for Thematic Learning in Elementary Schools

The revolution of industry 4.0 has begun to have an impact on the world of education.Learning must be based on the use of ICT (Information and Communication Technology).ICT-based learning media is more interesting than conventional media.In fact, this has not been fully done.This research aimed to find out how far the use of ICT for learning has been implemented by teachers in elementary schools and the impact on students.The research method used is qualitative.The research subjects were teachers and students from 5 elementary schools in Wonogiri Regency.The data were collected using observations, questionnaires and interviews.It was found that ICT-based learning media with the application of an interactive crossword puzzle has benefits including; it can be used as a media or source of learning and the utilization in schools depends on the facilities they have and the motivation of the teacher to use it.The students' involvement in learning to use ICT makes them become passionate about learning.ICT, which is a facility in schools, can be used as a stimulus for teachers to use it in learning.Therefore, we need an understanding for teachers as learning agents to utilize ICT so that they can do thematic learning in elementary schools with maximum results.
Yachay Yupay: A Dynamic Application for Learning in Mathematical Logical Reasoning for the Addition of Natural Numbers

Yachay Yupay, is a dynamic application that teachers can use, whose objective is to provide new strategies for teaching logical reasoning in the learning of arithmetic addition skills, and the development of cognitive ability in children Six years of the first grade of primary education in rural areas, in this way contribute to solve the difficulties children face during the acquisition of these skills. The type of research in this work is applied, explanatory level and pre-experimental design. This tool is didactic, it uses a visual approach to cognitive conflict to teach and learn mathematics in a fun way, teaching is not only verbal or textual, but is visually, it has different levels of difficulty that increase progressively, they used the performance indicators and the rating scale of the Learning Paths, where satisfactory results were obtained, both for teachers and students where the research was conducted, also to help reduce the digital divide that exists in the urban and rural areas.
Formel Ontolojiler ve Betimleyici Mantıklar

The history of ontology reveals various methodologies that examine being. Traditional ontology studies being qua being and categorizes it. Formal ontology determines the categories that are common to all entities and classifies them with formal languages using these categories as well. However, for over thirty years, formal ontologies have been studied and built outside of philosophy. The reason why ontology is separated from philosophy and becomes an interdisciplinary study is due to our need to make classifications and standardization in data and information management in accordance with reality. What lies behind this requirement is the exponential growth of data and the impossibility of analyzing it neither by humans nor by existing methods. For instance, dozens of studies are published every minute, and then scientists cannot follow all these developments with classical techniques. Therefore, the desire that machines should be involved in the processes of sharing and producing knowledge is far higher than ever. This desire, thus, has led us to develop new models for knowledge production. It has been experienced that the standardization of knowledge is essential in the construction of models; for, significant differences were found between taxonomic structures, concept definitions, and different granularity levels in the knowledge representations of various working groups, and even the integration of data was not possible. At this point, the selection, classification, and standardization of knowledge have been guaranteed by using ontological principles and methods. In this article, within information systems, we will introduce the formal ontologies, the formal theory covering the broadest knowledge of the field by selecting, classifying and standardizing it by using the principles and methods of philosophical ontologies. We will mention that we use description logics, a family of formal languages with the expressive power of natural language and high-performance reasoning algorithms, to represent this theory in machines. In essence, this article will introduce formal ontologies and description logics in the realm of knowledge representation.
Face Sketch Colorization via Supervised GANs

Face sketch recognition is one of the most challenging heterogeneous face recognition problems. The large domain difference of hand-drawn sketches and color photos along with the subjectivity/variations due to eye-witness descriptions and skill of sketch artists makes the problem demanding. Therefore, despite several research attempts, sketch to photo matching is still considered an arduous problem. In this research, we propose to transform a hand-drawn sketch to a color photo using an end to end two-stage generative adversarial model followed by learning a discriminative classifier for matching the transformed images with color photos. The proposed image to image transformation model reduces the modality gap of the sketch images and color photos resulting in higher identification accuracies and images with better visual quality than the ground truth sketch images.
RETRACTED: Model for evaluating the teaching effect of the college English public speaking course under the flipped classroom hybrid teaching mode with intuitionistic trapezoidal fuzzy numbers

This article has been retracted, and the online PDF has been watermarked “RETRACTION”. The retraction notice is available at http://doi.org/10.3233/KES-210080.
Text to Image Generation of Fashion Clothing

The recent exponential advancements in Generative Adversarial Networks have a major contribution in development of dynamic, highly compelling and fascinating image generation. This paper proposes an approach a framework that will accept text input from the user about the fashion pattern and the model will generate images of fashion clothing based on the text input. The model proposed can assist people be their own designers for creating a range of fashion clothing for themselves using the power of Deep Learning and Generative Adversarial Networks (GAN). Paper aims to engender realistic images using StackGAN model consisting of 2 stages, using highly-annotated dataset Fashion-Gen [4]. The initial stage generates low-resolution contours of the crude shape around a real-world human model based on the provided text description by the user. The second stage inputs stage one output to yield high resolution images with fascinating and detailed style-mapping to the text input. As compared to the precedent datasets, the dataset suggested to use is Fashion-Gen which is simply in resemblance with real-world and consists of High Definition quality images which would further lead to accurate explications. It is an exemplar till date as it provides a conglomeration of features with regards to parameters like angles, multitudinal categorization and sub-categorization of clothing. Such richly annotated dataset enables us to deploy powerful StackGAN model in plausible image generation. The model is then deployed on cloud for training and further use in the next stage of building an interactive web interface for the user to use the model efficiently.
DEHAZING RESEARCH ON BRIGHTNESS EQUALIZATION MODEL OF DRONE IMAGE

Abstract. Due to the rapid development of drone technology, aerial imagery of drones is increasingly used in various fields. However, the aerial image of the drone is highly susceptible to weather conditions during the imaging process. Most aerial images are inevitably affected by fog when they are acquired. Due to the scattering and absorption of the atmosphere, the aerial image of the drone in foggy days has the characteristics of low contrast and unclear scenery. Due to the scattering and absorption of the atmosphere, the aerial image of drone acquired in the foggy environment has the characteristics of low contrast and unclear scenery. The Defogging technology for aerial image of drone can obtain a large amount of useful information in a pictures with low information amount through a certain image processing method, and convert the image with low information amount into a useful image. Therefore, the image processing research carried out for such image degradation caused by natural phenomena has universal practical significance. Aiming at the problem that the aerial image of drone is often affected by haze and the image is blurred and the image quality is degraded, this paper proposes a new model for defogging aerial image of drone. The brightness equalization model is used to improve the degraded image with fog defects. The brightness equalization model obtains the brightness channel of the original image based on the HSI transform. The brightness equalization filter is used to dynamically adjust the brightness to the appropriate interval to achieve the purpose of defogging and then further optimizes the result image by using Gaussian blur and color reshaping. Two images with fog problems were compared, using the brightness equalization model of this paper. And the quality evaluation parameters are selected to evaluate the processing results of the dehazing model. The average value of the images processed by the model is more suitable and the main quality evaluation parameters such as standard deviation and entropy are better than those of the original image.The experimental results show that the brightness equalization model can effectively remove the influence of fog in the aerial image of the drone and improve the visual effect of the image.
Evolution of Modern Deep Learning Methods of Object Recognition

Vision is a very complex work of our brain.90% of the information entered to our brain is related to vision.In computer vision engineers and scientists are trying to give the computers the ability of vision.Object recognition is one of the most exciting fields of computer vision and AI.There is no rarity of problems and challenges in object recognition, from image classification to keypoint detection.But like many other problems in the world, there is still no obvious and "Best" way to resolve these problems.But the recent advancement in GPU technology propelled the success and accuracy of deep learning algorithms.In this paper, we will study about the evolution of deep learning methods in object recognition.We will also go through some of the classical machine learning methods for object recognition and talk about multimodal approaches to solving these issues.
Natural Language Query to NoSQL Generation Using Query-Response Model

This paper proposes an automated query-response model termed Natural Language Query to NoSQL Generation using Query-Response Model (NLNSM) that can manage various types of natural language queries from the user. The NLNSM System uses POS Tagging and combination based technique to handle assertive, interrogative, imperative, compound and complex type query sentences from the user. The algorithm of NLNSM generates NoSQL query for each of the natural language queries (NL) to retrieve data from the MongoDB database resident within the NLNSM. The NLNSM system is also able to extract exact noun or noun phrases from natural language queries posted by the user without any interruption. The usage of MongoDB has added advantage for the system. Unlike traditional databases, MongoDB uses JSON Scripts or Binary JSON (also termed as BSON) which is smaller and faster for computation with easier parsing techniques. Moreover, in MongoDB, one collection holds different documents where the number of fields, content and size of the document can differ from one document to another. It also supports dynamic queries on documents, is highly scalable and enables faster and efficient access of datasets.
SAT Attacks on ARX Ciphers with Automated Equations Generation

We propose a novel and simple approach to algebraic attack on block ciphers with the SAT-solvers.As opposed to a standard approach, the equations for key expansion algorithms are not included in the formulas that are converted to satisfiability problem.The lack of equations leads to finding the solution much faster.The method was used to attack a lightweight block ciphers -SIMON and SPECK.We report the timings for roundreduced versions of selected ciphers and discuss the potential factors affecting the execution time of our attack.
Application Based on Peruvian Sign Language for the Literacy of Parents of Deaf Children

In this research work, we developed an application based on Augmented Reality (RA) to teach parents or guardians of deaf children. The problem is that parents have serious difficulties in communicating with their children, because they do not use the same language for communication. An application based on Peruvian Sign Language was developed, using the Unity video game engine, the Leap Motion component, javascript and the C # programming language. This application allows parents or guardians of deaf children to be taught the sign language that allows them to communicate adequately with their children. The tests were conducted with 10 parents in the district of Andahuaylas in Apurimac-Peru. The results show that the parents or guardians are motivated to use the proposed application, and the learning time of the first 9 letters of the alphabet have reduced from 4.1 to 2.1 hours.
Unsupervised Representation Learning Based on Generative Adversarial Networks

This paper introduces a novel model for learning disentangled representations based on Generative Adversarial Networks. The training model is unsupervised without identity information. Unlike InfoGAN in which the disentangled representation is learnt by getting the variational lower bound of the mutual information indirectly, our method introduces a direct way by adding predicting networks and encoder into GANs and measuring the correlation among the encoder outputs. Experiment results on MNIST demonstrate that the proposed model is more generalizable and robust than InfoGAN. With experiments on Celeba-HQ, we show that our model can extract factorial features with complicate datasets and produce results comparable to supervised models.
Earthquake Damage Report Interactive Dashboard Using Bayesian Structural Time Series and Value-Suppressing Uncertainty Palettes

Earthquake Damage Report Interactive Dashboard addresses Mini Challenge 1 of VAST Challenge 2019. The project uses interactive visual analytics to report and summarize crowd-sourced damage reports temporally and spatially. We modelled the reports with Bayesian structural time series (BSTS) and encoded uncertainties in the reports visually with value-suppressing uncertainty palettes (VSUPs). The interactive visualization was developed with Vega and VueJS. We performed further analyses in Tableau. The dashboard and the challenge submission package can be found at https://vast2019.maxnadul.com.
Correction to: Hybrid Approach for IP Traceback Analysis in Wireless Networks

There was a typo in the second author’s name in the original article. The correct name is S. Karthik, as listed here. The original article has been corrected.
Sentiment Analysis of Digital India using Lexicon Approach

The sentiment analysis technique is come into view as a very useful medium to scrutinize the reviews and opinion of peoples. In current modern era peoples are showing their reviews with the help of social media. Digital India is most current topic for the discussion in India. It is novel idea and feasible solution of our daily routine in this rapid economic growth. The Indian Prime Minister has taken some feasible steps for Digital India. It is required to know the view and opinion of people on this Digital India scheme. In this paper, we propose a framework for analyzing the sentiments of peoples of Global India. Data is collected from the YouTube, select various videos and extract the comments that are given on those videos to analyze the sentiments. We select Lexicon approach to implement sentiment analysis with a help of The R language. This technique works on the basis of presumption of total polarity. Just instruct the Lexis with the help of group and collection of words. We also present Word Cloud of user comments.
Statistical Characteristics of English Entrance Exams of National Universities in Japan

In spite of the decreasing population of teenagers in Japan, the university entrance exams continue to be one of the toughest events where their intelligence is tested, which include their competency level of English. However, very few researches have conducted on English exams. This study is a part of the continuous study the researchers have started in 2017, which studied the English entrance exams of eight national universities. They were selected from the list according to the T-score; two schools from 4 levels. The current study added the results of 2018 exams of the same universities.Â Upon digitalizing their English exams, the researchers first measured their readability levels using Ozasa-Fukui Year Level. They also performed Correspondence Analysis to study the interrelations among the nine exams. All eight exams are measured to be 7 th year or higher by Ozasa-Fukui Year Level, which is created to measure English level according to the 6 years in the secondary school system in Japan. The correspondence analysis produced 7 dimensions. However, due to the unexpectedly high value of one exam, the other seven examsâ€™ interrelations became obscured; we have decided to perform another analysis without that particular exam. Its results showed that the seven exams could be categorized according to â€œbusiness and cultureâ€ and â€œfamily.â€ The researchers further employed Cluster Analysis to study all seven dimensions produced by Correspondence Analysis, which showed that Kyoto18 and Hokkaido18 have strong ties with each other. We believe this study has given us a definite starting point for the research on the university entrance exams in Japan. https://doi.org/10.26803/ijhss.11.6.2
The role of new media in building social skills of students with and without disabilities

Digital technologies are an integral part of our everyday life, and new media have changed the way we communicate.New forms of communication are most evident among school children, whose upbringing and education is marked by the immersion in the world of social media and internet technologies.The authors of this paper aim to analyze the influence of new media on the development of school children's social skills.It is based on a quantitative study of the impact of the length of daily usage of new media on elementary school students' social skills (with and without disabilities), seen from the perspective of both parents and teachers.The results were gained by the survey conducted on a representative sample of primary schools in the Republic of Croatia in the school year 2018/2019.Comparative analysis of the results obtained with similar research results led to a conclusion of the synergistic impact of new media and new communication channels on the development of social skills of students.The authenticity of this research is established in the approach involving both parents and teachers and the material on which the research was conducted (students with and without disabilities).The scientific contribution was achieved at different levels: at the cognitive level through the development of information concepts of social skills, while at the methodological level through the development of original methodology of qualitative comparison of respondents' attitudes by making a statistical analysis of dynamic ontological parameters.Finally, on the practical level through the application of defined information concepts in the study of the impact of new media on the development of social skills.