Reviewerly/exHarmony huggingface dataset:
EditGAN: High-Precision Semantic Image Editing

Generative adversarial networks (GANs) have recently found applications in image editing. However, most GAN based image editing methods often require large scale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Here, we propose EditGAN, a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks, e.g., drawing a new mask for the headlight of a car. EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing. Specifically, we embed an image into the GAN latent space and perform conditional latent code optimization according to the segmentation edit, which effectively also modifies the image. To amortize optimization, we find editing vectors in latent space that realize the edits. The framework allows us to learn an arbitrary number of editing vectors, which can then be directly applied on other images at interactive rates. We experimentally show that EditGAN can manipulate images with an unprecedented level of detail and freedom, while preserving full image quality.We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data. We demonstrate EditGAN on a wide variety of image types and quantitatively outperform several previous editing methods on standard editing benchmark tasks.
Examples of Optical Character Recognition Tools

Optical Character Recognition (OCR) refers to computer processes for converting images of printed, typed, or handwritten texts into text files. A computer requires OCR software to perform this task. This allows retrieving the text in the image and to save it in a file that...
Universidad y pensamiento complejo. Una reflexión urgente

Education, in all its cycles, allows us to notice what the new generations do, think and dream. Thus, university life reveals quite well what social dynamics have changed in the last twenty or thirty years in Colombia. Such institutions, considered by modernity as the centers par excellence for the creation of new knowledge, reveal that there is already a generation of students in the country who have new expectations; students who have been permeated by a society that was invaded by the new information and communication technologies -ICT-; a matter that makes them have conceptions of knowledge, truth and information very different from that of most of their teachers. This paper reflects on this situation, starting from the idea that, in Colombia, the University as an institution of knowledge is not up to the changes that have occurred in society and the proposals to face its own problems do not come out of it. of current life, given that it remained cloistered in a formation crossed by instrumental rationality, analytical thinking and its consequent partition of knowledge into multiple portions that do not reveal the complex relationships of the world that its students live. Hence, it is concluded that a university institution founded from a complex thought is necessary. This work is the result of reflections made from the doctoral work undertaken by the authors, based on an interpretive-comprehensive methodology, with a documentary analysis technique.
It's Only a Meme if It's from the Knowledge Organization Region of Information Science, Otherwise It's Just a Tweet.

In this short paper, we conducted a preliminary investigation of how internet memes use knowledge organization concepts in their transmission and communication of messages. Using classification, thesaural relationships, and metadata statements, we examined the employment of knowledge organization in the formula of a common internet meme and how the use of these concepts allowed meme creators to convey their argument.
Change Detection of Land Use and Land Covers in Buldhana District

The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to datafrom satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources.It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in U.S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.
Comunicación virtual para un aprendizaje colaborativo con estudiantes de Educación Básica

In the present work we aim to determine the influence of virtual communication on meaningful learning with students from the Fausto Molina Molina Basic Education School. Where the type of habit that schoolchildren do could be manifested, a survey was also carried out on an established population to reach the conclusions where the knowledge of virtual communication management and significant learning are tested. The schoolchildren of said educational entity were analyzed regarding the educational aspect and the implementation of digital platforms in the various investigations that they must comply with. In addition to this, it was learned that some of the educators are not completely involved in what is digital platforms. The methodology used in this research work is the inductive and deductive method, the population of 1397 was taken into account and the sample is 311 participants, after the respective analysis for a descriptive analysis and bibliographic data, it is concluded that the management of communication virtual and meaningful learning as it benefited listeners in their education. Giving significant results Considering attention can benefit collaborative learning, as well as improve interpersonal relationships that favor reducing knowledge in students; Teachers must modernize towards an adaptable teaching style before distance and virtual learning, delineate activities with inclusion, they would improve on the use of computational tools, acquiring skills to manage educational platforms.
Proving The Reading Ability of Children Aged 5-6 Years Through The Application of Picture Card Media

This study was conducted to determine efforts to improve the reading ability of children aged 5-6 years through the application of image media in group B in TK PGRI Melati 2. To obtain data in this study the author used quantitative research methods, from the data generated through observation, the format of ability assessment. This research was conducted in group B PGRI Melati 2 Kindergarten with a total of 15 students. The purpose of this study is to improve reading skills by using interesting media images so that children are able to read and recognize the letters of the words in each picture. Not only using pictures that were done in this study, but children can make their own sentences using the picture cards. There is an aspect of language that is taken in this case, so that children get a lot of vocabulary for each of their vocabulary. Teachers can make interesting games too with this picture media. Using image media is also able to improve children's cognitive in recognizing every object or word in the picture, so that children understand and understand the real form of the image. The success of research using picture cards is very helpful in improving each word
Automatic Generation of a Metric Report: A Case Study of Scientometric Analytics

Automatic report generation is an emerging technology that mechanically generates documents in the form of a report consisting of text, tables, and figures about a specific topic. This paper proposes a model for automated generation of a scientometric research analysis report for any selected country. The scholarly database utilized in this study is Microsoft Academic Graph. Given the two-letter code of the selected country, starting study year, and ending study year data concepts to the employed database, the model extracts the datasets including the scientific research information for the selected country in the specified period, and generates an in-depth analysis report about the country’s research publications. The model consists of two main stages. The first stage extracts the datasets for the selected country from the utilized database using Azure Databricks and Azure Blob Storage services. The second stage utilizes a predefined scientometric research analysis report for generating a new report for the selected country. A case study on big data analysis for Saudi Arabian research publications was conducted. An evaluation was performed on the report within 10 evaluators to understand the practicability of the proposed model. They evaluated the report through four-point criteria on the user perceptions. The result indicated that the model was able to successfully create quite a pleasing scientific report in term of factuality, coherency, sufficiency, and its ability to impart new knowledge for the readers.
Interactive Learning Approach for Arabic Target-Based Sentiment Analysis

Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis.In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language.The proposed IA-LSTM model uses an interactive attentionbased mechanism to force the model to focus on different parts (targets) of a sentence.We investigate the ability to use targets, right and left contexts, and model them separately to learn their own representations via interactive modeling.We evaluated our model on two different datasets: Arabic hotel review and Arabic book review datasets.The results demonstrate the effectiveness of using this interactive modeling technique for the Arabic targetbased sentiment analysis task.The model obtained accuracy values of 83.10 compared to SOTA models such as AB-LSTM-PC which obtained 82.60 for the same dataset.
Ocean Waste Detection Model

The number of marine debris is excellent in understanding the diagnosis of debris from all oceans of the world and the identification of the highest levels of waste disposal that is most necessary for the removal of waste. Currently, the standard for floating waste management requires the use of a manta trawl. Techniques that require manta trawls (or similar ground-collection devices) that use the physical removal of marine debris as a first step and then analyze the collected samples as a second step. The need for pre-analysis removal is very costly and requires significant oversight - preventing the safe transfer of marine waste monitoring services to all Earth's marine bodies. Without better monitoring methods and samples, the overall impact of water pollution on the entire environment. This study revealed an unusual flow of activity that used images taken from aquatic debris as roots. Produces quantification of marine plastic or waste incorporated into photographs to perform accurate quantification and body removal. This model is trained in the ImageNet Large Visual Recognition Challenge using the 2012 data and can distinguish between many different classes such as cardboard, glass, metal, paper, and plastic. This program uses the transfer of learning from the existing model and then returns it to separate a new set of images. Workflow involves creating and processing domain-specific information, building an object acquisition model using a deep neural network.
Real-time performance evaluation of BGSLibrary algorithms for intelligent surveillance

Background subtraction is the first and basic stage in video analysis and smart surveillance to extract moving objects. In fact, the background subtraction library was created by andrews sobral in 2012, which currently combines 43 background subtraction algorithms from the most popular and widely used in the field of video analysis.  Each algorithm has its own characteristics, strengths, and weaknesses in extracting moving objects. The e valuation allows the identification of these characteristics and helps researchers to design the best methods. Unfortunately, the literature lacks a comprehensive evaluation of the algorithms included in the library. Accordingly, the present work will evaluate these algorithms in the bgs-library through the segmentation performance, execution time, and processor, so as to, achieve a perfect, comprehensive, real-time evaluation of the system. Indeed, a BMC (Background Modeling Challenge) dataset was selected using the synthetic video with the presence of noise. Results are presented in tables, columns, and foreground masks.
CATFORD’S TRANSLATION SHIFTS USED IN TRANSLATING THE ANIMAL FARM NOVEL

ABSTRAK Penelitian ini untuk membuktikan apakah “translation shifts” masih relevan dipakai untuk pengalihan pesan dan produk terjemahan dengan stategi ini menghasilkan terjemahan yang relatif akurat, jenis dan strategi pergeseran apa yang dapat membantu penulis menyampaikan pesan dari Bahasa sumber ke Bahasa sasaran. Penelitian ini menggunakan penelitian kualitatif deskriptif dengan mengumpulkan dan menganalisis versi Bahasa Indonesia dan Inggris dari Animal Farm Novel menggunakan teori dari  JC Catford: Level Shit, Category Shift (Class Shift, Structure Shift, Unit Shift, Intra System Shift. Hasil analisis menunjukkan bahwa dari 233 data yang diambil terdapat 38% pergeseran unit, 25% pergeseran struktur, 18% pergeseran intra-sistem 15% pergeseran level dan 3% pergeseran kelas kata. Berdasarkan data yang dianalisis, penerjemah secara dominan menggunakan kategori pergeseran terjemahan yang diterapkan dalam menerjemahkan novel Animal Farm. Sebagai pendukung penulis juga menambahkan Analisa linguistic-ellipsis oleh ( Halliday and Hassan, 1976) dan analisa penerjemahan berdasarkan teori penilaian penerjemahan oleh ( Nababan et.al., 2012). diharapkan penelitian ini akan dapat meningkatkan wawasan penerjemah pemula dalam memahami teori-teori pergeseran terjemahan dan teori pendukung lainnya guna meningkatkan kemampuan mereka dalam menerjemahkan bahasa baik dari bahasa sumber ke bahasa sasaran dan juga sebaliknya. Kata kunci : Pergeseran terjemahan, ellipsis, penilaian penerjemahan, animal farm. ABSTRACT The purpose of this research is to demonstrate that the translation shifts  strategy is relevant to be employed in rendering thoughts and messages in translation process despite cultural differences. The research is using a descriptive qualitative method by collecting and analysing both the Indonesian and English versions of Animal Farm Novel using the theory by JC Catford: Level Shit, Category Shift (Class Shift, Structure Shift, Unit Shift, Intra System Shift). The result of this research shows that there were 233 data points which were analysed qualitatively to determine the types of shift and what strategies were employed. 38% of data are using unit shift, 25%  structure shift  18% Intra-system shift followed b y level shift with 15% of occurrence. The class shift took last place with 3% of occurrences. The author is assumed to have used the translation shift  theory as a means to dissect the problems encountered and evaluate the accuracy of the product of translation, the author also adds the linguistic-ellipsis theory by (Halliday and Hassan , 1976) and the translation quality assessment t heory by ( Nababan, dkk., 2012). It is hoped that this research paper will be of great use in furthering language development in terms of translation and can be another basic influence for translators to learn more about the translation shift theory to hone their translation skills. Key words: translation shifts, ellipsis, translation quality assessment, animal farm
Memory Efficient Meta-Learning with Large Images

Meta learning approaches to few-shot classification are computationally efficient at test time requiring just a few optimization steps or single forward pass to learn a new task, but they remain highly memory-intensive to train. This limitation arises because a task's entire support set, which can contain up to 1000 images, must be processed before an optimization step can be taken. Harnessing the performance gains offered by large images thus requires either parallelizing the meta-learner across multiple GPUs, which may not be available, or trade-offs between task and image size when memory constraints apply. We improve on both options by proposing LITE, a general and memory efficient episodic training scheme that enables meta-training on large tasks composed of large images on a single GPU. We achieve this by observing that the gradients for a task can be decomposed into a sum of gradients over the task's training images. This enables us to perform a forward pass on a task's entire training set but realize significant memory savings by back-propagating only a random subset of these images which we show is an unbiased approximation of the full gradient. We use LITE to train meta-learners and demonstrate new state-of-the-art accuracy on the real-world ORBIT benchmark and 3 of the 4 parts of the challenging VTAB+MD benchmark relative to leading meta-learners. LITE also enables meta-learners to be competitive with transfer learning approaches but at a fraction of the test-time computational cost, thus serving as a counterpoint to the recent narrative that transfer learning is all you need for few-shot classification.
IMPLEMENTASI TEKNIK STORYBOARDING DALAM PEMBUATAN FILM ANIMASI 3D PADA KISAH MUSH’AB BIN UMAIR

A historical story is a story taken from facts and events in the past that become the background of the occurrence of something that has historical value. In our lives to this day, of course, there have been many histories. Be it cultural, social, religious history and others. Speaking of the history of religion, of course all religions have their own history. With this, the author takes the history of the Islamic religion entitled the story of the journey of Mush'ab bin Umair. This story is about the journey of a friend of the apostle who willingly sacrificed everything for the sake of his religion. However, the story is still in book form. Based on the survey results, Indonesians prefer visualization over text. So that the author will implement the story into the form of 3D animation. In the process of making 3D animation has 3 stages, namely pre-production, production and post-production. The pre-production stage is the process of developing ideas and planning before the production process. In the pre-production process there is a stage that is a storyboard that serves to provide the details needed in the process of making 3D animated films. So that it can provide convenience for the director in arranging the animation to be created.
Is Information Density Uniform in Task-Oriented Dialogues?

The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted. In this paper, we test whether, and within which contextual units this principle holds in task-oriented dialogues. We show that there is evidence supporting the principle in written dialogues where participants play a cooperative reference game as well as in spoken dialogues involving instruction giving and following. Our study underlines the importance of identifying the relevant contextual components, showing that information content increases particularly within topically and referentially related contextual units.
ANALISIS ALIH KODE CAMPUR KODE INTERAKSI PEMBELAJARAN BAHASA INDONESIA SMP PGRI TUMBRASANOM

AbstractThis study aims to describe the from of code transfer and mix of Indonesian teacher code at SMP PGRI Tumbrasanom, as well the factors causing the transfer of code and code interaction in the interaction of learning Indonesian in class VII SMP PGRI Tumbrasanom. Subjects in this study were Indonesian teachers at SMP PGRI Tumbrasanom. The method used in this research is to use descriptive qualitative method. The source of this research data in the form of chapters of conversation in the interaction of learning Indonesian in class VII SMP PGRI Tumbrasanom. The results of this study indicate that the form of transfer of code on the interaction of learning Indonesian includes two types, namely, (1) transfer of internal code, (2) transfer of external code. The mixed form of code in the interaction of Indonesian learning is seen in terms of syntax include, (1) words, (2) phrases, (3) clauses. Factors of code change and code interference include: (a) the shift in speech, (b) changing the subject, (c) to telling the opposite, (d) to clarify information, (e) to be prestigious, and (f) adjust code used by the opponent said.Keyword: Code Transfer, Mixed Code
Hierarchical Concept Learning by Fuzzy Semantic Cells

Concept modeling and learning have been important research topics in artificial intelligence and knowledge discovery. This paper studies a hierarchical concept learning method that requires a small amount of data to achieve competitive performances. The method starts from a set of fuzzy prototypes called Fuzzy Semantic Cells (FSCs). As a result of FSC parameter optimization, it creates a hierarchical structure of data–prototype–concept. Experiments are conducted to demonstrate the effectiveness of our approach in a classification problem. In particular, when faced with limited training data, our proposed method is comparable with traditional techniques in terms of robustness and generalization ability.
Optical Recognition of Handwritten Logic Formulas Using Neural Networks

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.