Reviewerly/exHarmony huggingface dataset:
THE USE OF POSSE (PREDICT, ORGANIZE, SEARCH, SUMMARIZE, AND EVALUATE) STRATEGY TO IMPROVE STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXT AT THE TENTH GRADE OF SMK PGRI 2 DENPASAR IN THE ACADEMIC YEAR 2019/2020

Abstract&#x0D; This research aims to improve the tenth grade students’ reading comprehension by using POSSE strategy in SMK PGRI 2 Denpasar. It was an action research study which consisted of two cycles.The subjects of the research were students of X grade AK2 of SMK PGRI 2 Denpasar in the academic year 2019/2020. The qualitative data were collected by anlyzing the results of observation and interviewed the students on the learning process. The procedure of the research consisted of some stages, namely: planning, action, observation, and reflection. The instruments used of this research were observation sheet, interview guide, and tests. The method of collecting data were used interview, documentation, observation and test.&#x0D; The results of this research showed that the use of POSSE strategy was succesfully improve the students’ reading comprehension. Based on the quantitative data, the students’ mean scores for the reading comprehension improved from 55 in pre-test to 69 in post test 1 and 79 in post test 2. Meanwhile, from interviewed the students about the use of POSSE strategy showed that it helped the students to understand narrative text and helped them enrich their vocabulary knowledge. Besides that, the use of POSSE strategy could attract their attention in teaching and learning process.&#x0D; Keywords: Reading Comprehension, Narrative Text, POSSE strategy.&#x0D; &#x0D; Abstrak&#x0D; Tujuan dari penelitian ini adalah untuk meningkatkan pemahaman membaca siswa kelas sepuluh dengan menggunakan strategi POSSE di SMK PGRI 2 Denpasar. Penelitian ini adalah penelitian tindakan kelas yang terdiri dari 2 skilus. Subjek penelitian ini adalah terdiri dari 35 siswa kelas X AK2 di SMK PGRI 2 Denpasar tahun ajaran 2019/2020. Data kualitatif dikumpulkan melalui menganalisis hasil observasi dan interview siswa terhadap proses pembelajaran. Prosedur penelitian terdiri dari beberapa tahapan yaitu perencanaan, tindakan, observasi dan refleksi. Instrument dari penelitian ini menggunakan lembar observasi, pedoman wawancara, dan tes. Pengumpulan data menggunakan metode wawancara, dokumentasi, observasi, dan tes evaluasi.&#x0D; Hasil penelitian ini menunjukkan bahwa penggunaan strategi POSSE berhasil untuk meningkatkan pemahaman membaca siswa. Berdasarkan data kuantitatif, nilai rata-rata siswa untuk pemahaman membaca meningkat dari 55 di pre-test, menja1di 69 di post-test 1, dan 79 di post-test 2. Sementara itu, hasil wawancara para siswa tentang penggunaan strategi POSSE menunjukkan bahwa dapat membantu siswa untuk memahami teks naratif dan membantu mereka memperbanyak pengetahuan kosa kata. Selain itu, penggunaan strategi POSSE dapat menarik perhatian mereka dalam proses belajar mengajar.&#x0D; Kata Kunci: Pemahaman membaca, teks naratif, strategi POSSE.
INTEGRATION OF ICT METHODS FOR TEACHING AND LEARNING IN POST-COVID SCHOOLING. SELF-PERCEPTION AND TRUST ON DIGITAL COMPETENCES DEVELOPMENT OF ROMANIAN TEACHERS

Information about the paper titled "INTEGRATION OF ICT METHODS FOR TEACHING AND LEARNING IN POST-COVID SCHOOLING. SELF-PERCEPTION AND TRUST ON DIGITAL COMPETENCES DEVELOPMENT OF ROMANIAN TEACHERS" at IATED Digital Library
Building Interest in Agricultural Research Through User Education Activities

Agriculture is a very important sector in supporting the development of Indonesia. One effort to improve agricultural success is through research and development. Various innovations of technology in agriculture as the result of research and development produced by the Indonesian Agency for Agricultural Research and Development. Agricultural information generated needs to be introduced to the younger generation. For that, the Indonesian Centre for Agricultural Library and Technology Dissemination (ICALTD) sought to create collaborations with schools through user education for students. The materials were packaged in accordance with the level of student understanding in the form of audio-visual and printed materials. These activities are expected to provide an understanding of the importance of agriculture to the development of the nation as well as to foster a sense of interest in the world of research. This paper aims to provide an overview of collaboration between ICALTD and the school in library user education activities, particularly in the field of agriculture.
한국어교육에서의 연어 연구 동향 및 과제

This study aims to explore the achievement and limitation of research about the collocation in relation to Korean language education. Collocation are considered an important lexical grammatical unit in language education, and for this reason, collocation has also been noted for research in Korean vocabulary education. In order to examine specific research trends, the data was collected from the related previous studies and analyzed by research topics. A total of 121 data were analyzed, which were divided into five subject categories; the category of collocation from the perspective of Korean language education, selection and grading of lists of collocation for educational purposes, learner’s collocation errors, collocation education methods. This study investigates the current research trends to review the issues and to present the direction of future research.(Yonsei University)
Classification of Muslim Woman Student Using Artificial neural network

Hijab is a female genital covering commonly used by adult women. The number of hijab wearing fashion modes is developing, this makes one of the factors lack of understanding about using the correct hijab. By utilizing image processing using classification techniques can be distinguished between veiled women and not veiled. Artificial Neural Network (ANN) is an artificial intelligence that presents like a human brain by means of learning. ANN can be embedded into a computer program for the calculation process. One of the uses of ANN is to process the image to be classified. Image processing stages are data acquisition, preprocessing, edge detection, training, testing and classification. Based on the tests that have been carried out as many as 20 experiments, the results of image classification using Artificial Neural Network algorithm and backpropagation learning methods show a good level of accuracy.
DIGITAL IDENTIFIERS FOR LIBRARY SCIENTIFIC PUBLICATIONS

The value of the digital identifier DOI as a unique hyperlink for direct connection with a publication is underlined. The main requirements of the agency CrossRef for obtaining the digital identifier DOI are considered. The most famous international systems of unique identifiers of scientists, scientific publications, scientific issues as well the national open index of scientific citation of OUCI are considered. The process of assigning a unique DOI code to the scientific edition of Vernadsky National Library of Ukraine (VNLU) is highlighted.
The Influence of Media Use Characteristics on Social Relationships and School Adjustment through Adolescents' Self-Efficacy and Commitment

Recently, the use of media has become a daily life due to Covid-19, and education using media has expanded for teenagers. The purpose of this study is to empirically verify how media usage characteristics affect social relationships and school adaptation through the self-efficiency and immersion of teenagers at the youth education site. This study was collected and distributed questionnaires to 250 high school students in Seoul from November 14 to November 21, 2020. The results of the study were derived with a total of 249 questionnaires, excluding one missing questionnaire, and the hypothesis was verified using a Covariance Structure Analysis. Research has shown that, first, the existing, individual, and utility among the characteristics of using youth media have a significant effect on self-efficiency, respectively. Second, only entertainment among the characteristics of youth media use was found to have a significant effect on immersion. Third, immersion has a significant definition effect on school adaptation, and self-efficiency has been shown to have a definition impact on social relationships. Based on this study, we believe that it will be used as a fundamental resource for the development of youth media usage and policy direction in the age of the ever-changing Covid-19.
Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data

Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc. In multilingual communities around the world, a large amount of social media text is characterized by the presence of Code-Switching. Thus, it has become important to build models that can handle code-switched data. However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms. We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data. We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer. We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question - `Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?'. Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7\% (weighted F1 scores) when compared to supervised models trained for a two class problem.
Methodological Framework for the integration of Blockchain Technology in Coffee Industy

In recent years, blockchain technology has achieved remarkable application in multiple industries due to its inherent characteristics such as transparency, traceability, interoperability, integrity, and immutability, among others. This work presents a methodological framework for the integration of blockchain technology in the coffee industry. For this, several necessary steps are proposed to achieve the integration of this technology in this industry, from the analysis related to production aspects, definition of an architecture for the development of the solution and the development of the solution based on blockchain technology. This approach suggests that the characteristics of blockchain technology can be used to establish a solution that allows transparency and traceability in the production process carried out by the coffee industry.
Penerapan Data Mining Untuk Pengelompokan Minat Konsumen Terhadap Pengguna Jasa Pengiriman Pada PT. Jalur Nugraha Ekakurir (JNE) Pematangsiantar

Datamining is a method for finding knowledge in a large enough data pile by the process of digging and analyzing a very large amount of data in obtaining something that is true, new and useful so that it can be found in a pattern or pattern in the data. Customer service in general is every activity intended or intended to give satisfaction to customers, through this service the wishes and needs of customers can be met. In the Indonesian Dictionary explained that service is an attempt to serve the needs of others, while serving is to help prepare (help what someone needs). essentially service is a series of activities that constitute a process. As the service process takes place routinely and continuously covers the entire life of people in the community, the process of meeting needs through the activities of others including JNE is also a company that offers services engaged in shipping and logistics based in Jakarta, Indonesia. The official name is Tiki Path Nugraha Ekakurir (Tiki JNE) K-Means Algorithm is an iterative clustering algorithm that partitions data sets into a number of K clusters that have been set at the beginning.
Dense Coding Capacity in Correlated Noisy Channels with Weak Measurement

Capacity of dense coding via correlated noisy channel is greater than that in uncorrelated noisy channel. It is shown that weak measurement and reversal measurement can make further effort to improve quantum dense coding capacity in correlated amplitude damping channel, but this effort is very small in correlated phase damping channel and correlated depolarizing channel.
The plot( ) Function

The plot() function in R creates a graphic from objects of specific R classes. A figure resulting from a call to plot() can contain text, lines, points, and/or images, and the areas of the graphic can be filled by colors or patterns. The kind of graphic displayed depends on the class of the object(s) to be displayed. For example, a single time series (an object of class ts) gives a line plot that is plotted over time.
THE PHENOMENON OF FASCINATIVE EFFECT OF THE TEXT IN DISCOURSE WITH RESPECT TO THE TRANSLATION AND LANGUAGE TEACHING (BASED ON THE ITALIAN POLITICAL DISCOURSE)

Information about the paper titled "THE PHENOMENON OF FASCINATIVE EFFECT OF THE TEXT IN DISCOURSE WITH RESPECT TO THE TRANSLATION AND LANGUAGE TEACHING (BASED ON THE ITALIAN POLITICAL DISCOURSE)" at IATED Digital Library
An Object Detection based Solver for Google's Image reCAPTCHA v2

Previous work showed that reCAPTCHA v2's image challenges could be solved by automated programs armed with Deep Neural Network (DNN) image classifiers and vision APIs provided by off-the-shelf image recognition services. In response to emerging threats, Google has made significant updates to its image reCAPTCHA v2 challenges that can render the prior approaches ineffective to a great extent. In this paper, we investigate the robustness of the latest version of reCAPTCHA v2 against advanced object detection based solvers. We propose a fully automated object detection based system that breaks the most advanced challenges of reCAPTCHA v2 with an online success rate of 83.25%, the highest success rate to date, and it takes only 19.93 seconds (including network delays) on average to crack a challenge. We also study the updated security features of reCAPTCHA v2, such as anti-recognition mechanisms, improved anti-bot detection techniques, and adjustable security preferences. Our extensive experiments show that while these security features can provide some resistance against automated attacks, adversaries can still bypass most of them. Our experimental findings indicate that the recent advances in object detection technologies pose a severe threat to the security of image captcha designs relying on simple object detection as their underlying AI problem.
Implementing Visual Analytics Pipelines with Simulation Data

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.
Deep Q-Learning

In this chapter, we will do a deep dive into Q-learning combined with function approximation using neural networks. Q-learning in the context of deep learning using neural networks is also known as Deep Q Networks (DQN). We will first summarize what we have talked about so far with respect to Q-learning. We will then look at code implementations of DQN on simple problems followed by training an agent to play Atari games. Following this, we will extend our knowledge by looking at various modifications that can be done to DQN to improve the learning, including some very recent and state-of-the-art approaches. Some of these approaches may involve a bit of math to understand the rationale for these approaches. However, we will try to keep the math to a minimum and include just the required details to appreciate the background and reasoning. All the examples in this chapter will be coded using either the PyTorch or TensorFlow library. Some code walk-throughs will have the code for both PyTorch and TensorFlow, while others will be discussed using only PyTorch.
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition

This paper proposes an adaptation method for end-to-end speech recognition. In this method, multiple automatic speech recognition (ASR) 1-best hypotheses are integrated in the computation of the connectionist temporal classification (CTC) loss function. The integration of multiple ASR hypotheses helps alleviating the impact of errors in the ASR hypotheses to the computation of the CTC loss when ASR hypotheses are used. When being applied in semi-supervised adaptation scenarios where part of the adaptation data do not have labels, the CTC loss of the proposed method is computed from different ASR 1-best hypotheses obtained by decoding the unlabeled adaptation data. Experiments are performed in clean and multi-condition training scenarios where the CTC-based end-to-end ASR systems are trained on Wall Street Journal (WSJ) clean training data and CHiME-4 multi-condition training data, respectively, and tested on Aurora-4 test data. The proposed adaptation method yields 6.6% and 5.8% relative word error rate (WER) reductions in clean and multi-condition training scenarios, respectively, compared to a baseline system which is adapted with part of the adaptation data having manual transcriptions using back-propagation fine-tuning.
Pluralistic Image Completion

Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for \textbf{pluralistic image completion} -- the task of generating multiple and diverse plausible solutions for image completion. A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higher-quality completion results, but also with multiple and diverse plausible outputs.
Graph Representation Learning Using Attention Network

Network embedding is a method to learn low dimensional representation of nodes in large graph with the goal of capturing and preserving the network structure. Graph convolution networks (GCN) are successfully applied in node embedding task as they can learn sparse and discrete dependency in the data. Most of the existing work in GCN requires costly matrix operation. In this paper, we proposed a graph neighbor Sampling, Aggregation, and ATtention (GSAAT) framework. That does not need to know the graph structure upfront and avoid costly matrix operation. The proposed method first learn to aggregate the information of node’s neighbors and stacked a layer in which nodes are able to attend over the aggregated information of their neighbors feature. The proposed method achieved state-of-art performance in two classification benchmark: Cora, and Citeseer citation network dataset.
Mutual Graph Learning for Camouflaged Object Detection

Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an ideal model should be able to seek valuable, extra clues from the given scene and incorporate them into a joint learning framework for representation co-enhancement. With this inspiration, we design a novel Mutual Graph Learning (MGL) model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. Specifically, MGL decouples an image into two task-specific feature maps -- one for roughly locating the target and the other for accurately capturing its boundary details -- and fully exploits the mutual benefits by recurrently reasoning their high-order relations through graphs. Importantly, in contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations to maximize information interactions. Experiments on challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods.
Influencing Reinforcement Learning through Natural Language Guidance

Interactive reinforcement learning agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that is either positive or negative. While informative, this information can be difficult to generalize on its own. In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known Interactive reinforcement learning technique. Usually policy shaping employs a human feedback policy to help an agent to learn more about how to achieve its goal. In our case, we replace this human feedback policy with policy generated based on natural language advice. We aim to inspect if the generated natural language reasoning provides support to a deep reinforcement learning agent to decide its actions successfully in any given environment. So, we design our model with three networks: first one is the experience driven, next is the advice generator and third one is the advice driven. While the experience driven reinforcement learning agent chooses its actions being influenced by the environmental reward, the advice driven neural network with generated feedback by the advice generator for any new state selects its actions to assist the reinforcement learning agent to better policy shaping.