Reviewerly/exHarmony huggingface dataset:
MODEL PEMBELAJARAN MENULIS TEKS DRAMA MENGGUNAKAN MEDIA TEKS CERPEN SISWA KELAS VIII

Various learning models and media can improve students' skills in writing drama texts. This study aims to describe the application of learning models to writing drama texts with short story media and to describe the ability to write student drama texts using short story text media. This research uses descriptive quantitative research design. The results showed that in general the learning model using short story text media was effectively used for learning to write drama texts.
universitas negeri padang Pengembangan media pembelajaran berbasis android menggunakan aplikasi android studio materi ppkn untuk smk

AbstractUtilization of technology in various fields in the era of globalization becomes the main thing, especially in the field of education. The creation of technology-oriented learning media innovation is in the spotlight for educational institutions, because it is practical and easy to use. Making Android-based learning media is very diverse, involving elements of audio, visual, and audio visual. The software that the author uses for making android-based learning media is Android Studio software. This research is a research development known in the English term Research & Development (R & D). The model used is the modified Borg and Gall model. The resulting product has gone through a validation or assessment process with three media expert lecturers and one PPKn Vocational school teacher. Data collection in the form of documentation and questionnaire sheets. The subjects of this study were students of class X vocational school with PPKn subject matter. After going through the media feasibility test process it can be concluded that the developed media is suitable for use. In the assessment of media aspects by validator I obtained an average value of 4.91 categorized as Eligible validator II of 4.83 categorized as Eligible validator III of 4 was categorized as Eligible, while for the material aspects of the Valid with an average value average of 4,70. Keywords: Development, Learning Media, Android, Android Studio
Multi-Image Super Resolution in Multi-Contrast MRI

Acquisition of high-resolution magnetic resonance images (MRI) under distinct contrasts can enhance diagnostic information required in clinical diagnosis. Yet, acquiring high-resolution images might be impractical due to increased noise, prolonged scan durations and hardware costs. In such situations, an alternative solution can be the synthesis of high-resolution images from low-resolution images. Common methods perform super resolution of a single image. However, in multi-contrast MRI, the images of a single contrast might not contain sufficient prior information required for a successful deblurring. To enhance the required prior information, complementary prior information available in other contrasts can be used. Here, a multi-contrast MRI super resolution method is proposed to simultaneously deblur the images of multiple distinct contrasts. The proposed method relies on generative adversarial networks that can produce as realistic images as possible by better recovering high-frequency details. Qualitative and quantitative evaluations on a multi-contrast MRI dataset demonstrated that the proposed method outperforms the alternative single image MRI super resolution method.
A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromise Solution for Measuring the Degree of Infection With Corona Virus Disease (Covid-19)

A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
Unsupervised Discovery of Interpretable Latent Manipulations in Language VAEs

Language generation models are attracting more and more attention due to their constantly increasing quality and remarkable generation results. State-of-the-art NLG models like BART/T5/GPT-3 do not have latent spaces, therefore there is no natural way to perform controlled generation. In contrast, less popular models with explicit latent spaces have the innate ability to manipulate text attributes by moving along latent directions. For images, properties of latent spaces are well-studied: there exist interpretable directions (e.g. zooming, aging, background removal) and they can even be found without supervision. This success is expected: latent space image models, especially GANs, achieve state-of-the-art generation results and hence have been the focus of the research community. For language, this is not the case: text GANs are hard to train because of non-differentiable discrete data generation, and language VAEs suffer from posterior collapse and fill the latent space poorly. This makes finding interpetable text controls challenging. In this work, we make the first step towards unsupervised discovery of interpretable directions in language latent spaces. For this, we turn to methods shown to work in the image domain. Surprisingly, we find that running PCA on VAE representations of training data consistently outperforms shifts along the coordinate and random directions. This approach is simple, data-adaptive, does not require training and discovers meaningful directions, e.g. sentence length, subject age, and verb tense. Our work lays foundations for two important areas: first, it allows to compare models in terms of latent space interpretability, and second, it provides a baseline for unsupervised latent controls discovery.
Reallocation of Virtual Machines to Cloud Data Centers to Reduce Service Level Agreement Violation and Energy Consumption Using the FMT Method

Due to the increased use of cloud computing services, cloud data centers are in search of solutions in order to better provide the services demanded by their users. Virtual machine consolidation is an appropriate solution to the trade-off between power consumption and service level agreement violation. The present study aimed to identify low, medium, and high load identification techniques, as well as the energy consumption and SLAv to minimize. In addition to the reduced costs of cloud providers, these techniques enhance the quality of the services demanded by the users. To this end, reallocation of resources to physical hosts was performed at the medium load level using a centralized method to classify the physical hosts. In addition, quartile was applied in each medium to reduce the energy consumption parameters and violation level. The three introduced SMT - NMT and FMT methods for reallocation of resources were tested and the best results were compared with previous methods.The proposed method was evaluated using the Cloudsim software with real Planet Lab data and five times run, the simulation results confirmed the efficiency of the proposed algorithm, which tradeoff between decreased the energy consumption and service level of agreement violation (SLAv) properly.
HetSAGE: Heterogenous Graph Neural Network for Relational Learning

This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way how information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that on CORA, MUTA188 and MovieLens our approach outperforms the state-of-the-art in NSL.
PriorityCut: Occlusion-aware Regularization for Image Animation

Image animation generates a video of a source image following the motion of a driving video. Self-supervised image animation approaches do not require explicit pose references as inputs, thus offering large flexibility in learning. State-of-the-art self-supervised image animation approaches mostly warp the source image according to the motion of the driving video, and recover the warping artifacts by inpainting. When the source and the driving images have large pose differences, heavy inpainting is necessary. Without guidance, heavily inpainted regions usually suffer from loss of details. While previous data augmentation techniques such as CutMix are effective in regularizing non-warp-based image generation, directly applying them to image animation ignores the difficulty of inpainting on the warped image. We propose PriorityCut, a novel augmentation approach that uses the top-k percent occluded pixels of the foreground to regularize image animation. By taking into account the difficulty of inpainting, PriorityCut preserves better identity than vanilla CutMix and outperforms state-of-the-art image animation models in terms of the pixel-wise difference, low-level similarity, keypoint distance, and feature embedding distance.
Public policies for the adaptive management of covid-19: New normality in Colombia (2020)

Through two quantitative longitudinal studies, we explored socio-demographic and psychosocial dimensions of 2,722 Colombians in the context of the pandemic on: government measures, coping with the personal economic situation and socio-emotional climate;to identify coping constraints and propose public policy guidelines on strategies to adapt to the new normality The results are consistent with the theory Among the findings we highlight: the need to articulate policies with strategies that allow for the channeling of anxiety and stress;avoid individualistic attitudes versus social trust;and disseminate clear, simple and transparent narratives Likewise, we recommend particular measures for groups with differential affectation, related to socioeconomic conditions and gender © 2020, Universidad Nacional de Colombia All rights reserved
Symbol-Shift Equivariant Neural Networks

Neural networks have been shown to have poor compositionality abilities: while they can produce sophisticated output given sufficient data, they perform patchy generalization and fail to generalize to new symbols (e.g. switching a name in a sentence by a less frequent one or one not seen yet). In this paper, we define a class of models whose outputs are equivariant to entity permutations (an analog being convolution networks whose outputs are invariant through translation) without requiring to specify or detect entities in a pre-processing step. We then show how two question-answering models can be made robust to entity permutation using a novel differentiable hybrid semantic-symbolic representation. The benefits of this approach are demonstrated on a set of synthetic NLP tasks where sample complexity and generalization are significantly improved even allowing models to generalize to words that are never seen in the training set. When using only 1K training examples for bAbi, we obtain a test error of 1.8% and fail only one task while the best results reported so far obtained an error of 9.9% and failed 7 tasks.
Latent Space Exploration and Functionalization of a Gated Working Memory Model Using Conceptors

Working memory is the ability to maintain and manipulate information. We introduce a method based on conceptors that allows us to manipulate information stored in the dynamics (latent space) of a gated working memory model. This latter model is based on a reservoir: a random recurrent network with trainable readouts. It is trained to hold a value in memory given an input stream when a gate signal is on and to maintain this information when the gate is off. The memorized information results in complex dynamics inside the reservoir that can be faithfully captured by a conceptor. Such conceptors allow us to explicitly manipulate this information in order to perform various, but not arbitrary, operations. In this work, we show (1) how working memory can be stabilized or discretized using such conceptors, (2) how such conceptors can be linearly combined to form new memories, and (3) how these conceptors can be extended to a functional role. These preliminary results suggest that conceptors can be used to manipulate the latent space of the working memory even though several results we introduce are not as intuitive as one would expect.
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process

Contemporary coding education often present students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While pedagogically compelling, grading such student programs requires dynamic user inputs, therefore they are difficult to grade by unit tests. In this paper we formalize the challenge of grading interactive programs as a task of classifying Markov Decision Processes (MDPs). Each student's program fully specifies an MDP where the agent needs to operate and decide, under reasonable generalization, if the dynamics and reward model of the input MDP conforms to a set of latent MDPs. We demonstrate that by experiencing a handful of latent MDPs millions of times, we can use the agent to sample trajectories from the input MDP and use a classifier to determine membership. Our method drastically reduces the amount of data needed to train an automatic grading system for interactive code assignments and present a challenge to state-of-the-art reinforcement learning generalization methods. Together with Code.org, we curated a dataset of 700k student submissions, one of the largest dataset of anonymized student submissions to a single assignment. This Code.org assignment had no previous solution for automatically providing correctness feedback to students and as such this contribution could lead to meaningful improvement in educational experience.
A Prospection for Color Image Quantization by different Artificial Based Algorithms

For many computer and communications applications, colour quantization(CQ) is one of the significant preprocessing phases. Colour image quantization(CQ) is the approach that minimizes the size of the image, decreases the colour space of that image while maintaining the visual quality of that image, which makes it effective for storage as well as for communication. Two measures are involved in image colour quantization. The first phase is to create a selection of representative colours such that a color palette can be achieved. The second pixel should be mapped to one color of each image in the color palette(CP). This paper presents a literature review of basic Color image quantization techniques from the last five years. Recent research in each of the Color image quantization technique is presented in this paper.Keywords-: Color quantization; Bee colony; k-means; Image compression; Artificial Ant Algorithm; Swarm Algorithms
Assessing Patron Driven Acquisition (PDA) adoption among Malaysian academic libraries

This study aims to explore the adoption of the Patron Driven Acquisition (PDA) model among Malaysian academic libraries. The study ensues the continuous increase in the proportion and cost of information resources (electronic and print) coupled with the current dwindling budget crushing libraries worldwide. It adopts questionnaires to elicit responses from 111 purposively selected librarians, specifically from Malaysia's academic libraries' acquisition units. This study revealed that some Malaysian academic libraries practice the user-initiated collection acquisition model, with modifications from the Patron Driven Acquisition (PDA) practiced in the developed world. Malaysian academic libraries adopt the stand-alone user-initiated collection development model instead of integrating the model with either the library OPAC or publishers ‘databases as practiced by other libraries. The user-driven acquisition model is productive considering resource usage, library budget justification, and high return on investment (ROI), according to Malaysian academic libraries.
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic

Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity. Nonetheless, these products are accumulated using high-precision (typically 32-bit) additions, an operation that dominates the arithmetic complexity of inference when using extreme quantization (e.g., binary weights). To further optimize inference, we propose WrapNet that adapts neural networks to use low-precision (8-bit) additions in the accumulators, achieving classification accuracy comparable to their 32-bit counterparts. We achieve resilience to low-precision accumulation by inserting a cyclic activation layer, as well as an overflow penalty regularizer. We demonstrate the efficacy of our approach on both software and hardware platforms.
CoreTrustSeal plus FAIR Overview

This document represents the third alignment of CoreTrustSeal to FAIR requirements to inform repositories seeking to enable FAIR data. Further context is presented in the FAIRsFAIR project milestone: https://doi.org/10.5281/zenodo.4003598.
The effect of Using Flipbook-Based E-modules on Student Learning Outcomes (Similarity Check)

The creative innovation in education in the era of revolution 4.0 is the use of electronic-based teaching materials as a source of student learning. This study aims to examine the effect of using a flipbook-based e-module on transverse wave material with a one group pretest posttest design research design. The research method that has been used is quasi-experimental research design with one group pretest posttest design. The research sample was students of Sriwijaya University Physics Education. Data were analyzed using N-gain analysis, normality test, homogeneity test and Paired Sample T-test. The results showed that the posttest test results were higher than the pretest tests with an average of 33.28, while the normality test results obtained pretest data of 0.026 and posttest data of 0.257. Finally, the N-gain test results were obtained at 0.466 in the moderate category, it was concluded that there was an effect of using flipbook-based e-modules on dynamic electricity on student learning outcomes, so the use of flipbook-based e-modules was effectively used as teaching material on student learning outcomes.
DEVELOPMENT OF WEB-BASED LEARNING IMPLEMENTATION AND ANDROID APPLICATIONS TO BRING TEACHER CAPABILITY

This study aims to determine the results of the process of developing a web-based e-learning implementation book and an android application, as a way to hone the teaching skills of prospective teachers. The instruments in this study were observation sheets, books, and questionnaires. Based on the achievement of the product, product evaluation is needed including validation of material experts and media experts. The stages in this study with the ADDIE model are (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. The results of this study obtained that the web-based e-learning implementation book and android application have fulfilled (1) Validation data with the average results obtained from the media validation sheet from media experts 93.67% and 85 material experts, 33%. (2) seen a positive influence from the results of the post-test with a percentage of 91.68% and (3) shows the response of prospective teachers after using e-learning books in the main field test showed a percentage of 82.68% so that they can hone their ability to teach independently.
Parsimonious relations between Guitar Textural Proposals

Guitar Textural Analysis is an analytical proposal devised for the composition for the guitar that exposes the relations between the textural structure and the technical procedures used in the instrumental performance. The Guitar Textural Proposals (GTPs) are the specific configurations extracted in this analytical process. In the present work, the focus remains on recognizing parsimonious movements between GTPs, that is, minimal distinctions between patterns of use of the instrument combined with respective textural configurations. Parsimoniousness is defined in the context of Developing Variation, Neo-Riemannian Theory, and Partitional Analysis. Previous works by Fabio Adour and authors Sérgio Freire and Pedro Cambraia present perspectives on the relation between texture and guitar performance. Leo Brouwer's series of progressive pieces Études Simples is adopted as a reference. The work results in a proposal for a ordered network of technical-textural configurations connected by parsimonious relations.
Simple Fusion of Object Detectors for Improved Performance and Faster Deployment

Object detectors often suffer from multiple performance limitations which may be attenuated with larger training datasets, improved training techniques, and complex detection models. However, such strategies are complex and time-consuming for applications requiring fast deployments. We propose a Simple Fusion of Object Detectors (SFOD) late ensemble method to combine existing pre-trained, off-the-shelf, fine-tuned object detectors and leverage on their divergences to improve the overall detection performance. Comprehensive experimental evaluations, based on PASCAL VOC07 challenge, demonstrate SFOD's ability to improve mean average precision ( mAP) for different fusion sizes and base detector combinations, reaching an absolute 84.08% mAP and an improvement of 3.97% mAP. The improvements extend to most classes, fusion sizes, and base detector combinations, revealing AP improvements up to 17.35% over baselines, for particular object classes. Practical application evaluations, based on optimal threshold selection, also reveal improvements of 10.54% and 8.36% of mean recall ( mR) and mAP, respectively. Our approach does not require additional training and is quickly deployable, yet providing a few adjustable hyperparameters to optimize the recall-precision relation for specific applications. Improvements obtained from our proposed SFOD fusion pipeline span across a broad range of object classes and are important for a wide variety of critical applications where every successful detection is treasured.