Reviewerly/exHarmony huggingface dataset:
Peningkatan Kualitas Pendidikan Melalu Penerapan E-Learning

Along with the development of the era of globalization, the education system around the world has also undergone changes, especially regarding the learning of students. Recently, developments have started from the online learning system or E-Learning. Or have begun to change because of the increasingly growing digital platforms. Meanwhile, the e-learning industry in Indonesia has not been able to develop better. The main objective of this study is to understand the barriers to implementing e-learning in Indonesia apart from the benefits of online education, such as lower costs and pedagogical benefits. The researcher proposes an initial conceptual framework that explains the steps and intentions of students to switch to utilizing e-learning. In our future research, researchers will refine and test conceptual frameworks and provide guidelines for higher education institutions and policy makers on institutional change to support the use of e-learning.
Arquivamento Web : legislação correlata

This is an introductory analysis, based on a qualitative research in the databases of the Chamber of Deputies of the current federal legislation and legislative proposals related directly or indirectly to the capture and preservation of public institutions' websites, that is, to the concept of web archiving. The approach is made under the analysis of the scope of the definition of legal deposit of publications present in the Brazilian legislation, confronting it, in passing, with the understanding that the institutional websites are records and need to be preserved as archives.
STUDENTS’ PERCEPTION ON THE IMPLEMENTATION OF ENGLISH LANGUAGE TEACHING IN SMK MUHAMMADIYAH 3 BUMIAYU

This study aimed at finding out the students’ perception on the implementation of English Language Teaching. The subjects of this research were 70 students of SMK Muhammadiyah 3 Bumiayu, Brebes with the total number was two (3) teachers. The subjects were determined by the intention to obtain information through interviews and observations. Considering the responses from the questionnaire, It can be summed up that the students’ perceptions to English teaching learning process are positive. However, it was noted that there are almost 50 % of the students who have difficulties in mastering English and they are also still less-autonomy in learning English by themselves before the lesson given. On the other hand, the students also think that the time duration for English teaching learning process is inadeguate
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss.

The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. In this paper, we investigate three different approaches to improve DNNs in recognizing objects in OoD orientations and illuminations. Namely, these are (i) training much longer after convergence of the in-distribution (InD) validation accuracy, i.e., late-stopping, (ii) tuning the momentum parameter of the batch normalization layers, and (iii) enforcing invariance of the neural activity in an intermediate layer to orientation and illumination conditions. Each of these approaches substantially improves the DNN's OoD accuracy (more than 20% in some cases). We report results in four datasets: two datasets are modified from the MNIST and iLab datasets, and the other two are novel (one of 3D rendered cars and another of objects taken from various controlled orientations and illumination conditions). These datasets allow to study the effects of different amounts of bias and are challenging as DNNs perform poorly in OoD conditions. Finally, we demonstrate that even though the three approaches focus on different aspects of DNNs, they all tend to lead to the same underlying neural mechanism to enable OoD accuracy gains -- individual neurons in the intermediate layers become more selective to a category and also invariant to OoD orientations and illuminations.
Risk and return prediction for pricing portfolios of non-performing consumer credit

We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the need for trading portfolios formed by overdue loans as a manner of risk transferring. However, the problem is nontrivial technically and related research is absent. We tackle the challenge by building a bottom-up architecture, in which we model the distribution of every single loan's repayment rate, followed by modeling the distribution of the portfolio's overall repayment rate. To address the technical issues encountered, we adopt the approaches of simultaneous quantile regression, R-copula, and Gaussian one-factor copula model. To our best knowledge, this is the first study that successfully adopts a bottom-up system for analyzing credit portfolio risks of consumer loans. We conduct experiments on a vast amount of data and prove that our methodology can be applied successfully in real business tasks.
Defensive Tensorization.

We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network. The layers of a network are first expressed as factorized tensor layers. Tensor dropout is then applied in the latent subspace, therefore resulting in dense reconstructed weights, without the sparsity or perturbations typically induced by the randomization.Our approach can be readily integrated with any arbitrary neural architecture and combined with techniques like adversarial training. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We validate the versatility of our approach across domains and low-precision architectures by considering an audio classification task and binary networks. In all cases, we demonstrate improved performance compared to prior works.
Localized Super Resolution for Foreground Images using U-Net and MR-CNN

Images play a vital role in understanding data through visual representation. It gives a clear representation of the object in context. But if this image is not clear it might not be of much use. Thus, the topic of Image Super Resolution arose and many researchers have been working towards applying Computer Vision and Deep Learning Techniques to increase the quality of images. One of the applications of Super Resolution is to increase the quality of Portrait Images. Portrait Images are images which mainly focus on capturing the essence of the main object in the frame, where the object in context is highlighted whereas the background is occluded. When performing Super Resolution the model tries to increase the overall resolution of the image. But in portrait images the foreground resolution is more important than that of the background. In this paper, the performance of a Convolutional Neural Network (CNN) architecture known as U-Net for Super Resolution combined with Mask Region Based CNN (MR-CNN) for foreground super resolution is analysed. This analysis is carried out based on Localized Super Resolution i.e. We pass the LR Images to a pre-trained Image Segmentation model (MR-CNN) and perform super resolution inference on the foreground or Segmented Images and compute the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics for comparisons.
Cyclic arcs of Singer type and strongly regular Cayley graphs over finite fields

In [32], the first author gave a construction of strongly regular Cayley graphs on the additive group of finite fields by using three-valued Gauss periods. In particular, together with the result in [4], it was shown that there exists a strongly regular Cayley graph with negative Latin square type parameters (q6,r(q3+1),−q3+r2+3r,r2+r), where r=M(q2−1)/2, in the following cases: (i) M=1 and q≡3(mod4); (ii) M=3 and q≡7(mod24); and (iii) M=7 and q≡11,51(mod56). The existence of strongly regular Cayley graphs with the above parameters for odd M>7 was left open. In this paper, we prove that if there is an h, 1⩽h⩽M−1, such that M|(h2+h+1) and the order of 2 in (Z/MZ)× is odd, then there exist infinitely many primes q such that strongly regular Cayley graphs with the aforementioned parameters exist.
The Research of Small Wave Basis ANN Algorithm and 5-Class Decision Factor AI

Abstract The accuracy and reliability of continuous space curve estimation is the key to global exploration. An improved artificial intelligence algorithm is proposed for the analysis of continuous space. First, small wave basis ANN algorithm is proposed to solve discretization strategy in continuous space: The hidden layer node transfer function in BP neural network is substituted with wavelet basis function, while the replaced BP neural network is composed of wavelet neural network. Secondly, improved wolf algorithm is set up. The core wolf system ensures the precision of whole exploration. Finally, the main and auxiliary double cores and five-class decision factor is used to establish a population classification model to solve the convergence of the algorithm.
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs

Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query. Embedding-based methods solve both tasks by first computing an embedding for each entity and relation, then using them to form predictions. However, existing scalable KG embedding frameworks only support single-hop knowledge graph completion and cannot be applied to the more challenging multi-hop reasoning task. Here we present Scalable Multi-hOp REasoning (SMORE), the first general framework for both single-hop and multi-hop reasoning in KGs. Using a single machine SMORE can perform multi-hop reasoning in Freebase KG (86M entities, 338M edges), which is 1,500x larger than previously considered KGs. The key to SMORE's runtime performance is a novel bidirectional rejection sampling that achieves a square root reduction of the complexity of online training data generation. Furthermore, SMORE exploits asynchronous scheduling, overlapping CPU-based data sampling, GPU-based embedding computation, and frequent CPU--GPU IO. SMORE increases throughput (i.e., training speed) over prior multi-hop KG frameworks by 2.2x with minimal GPU memory requirements (2GB for training 400-dim embeddings on 86M-node Freebase) and achieves near linear speed-up with the number of GPUs. Moreover, on the simpler single-hop knowledge graph completion task SMORE achieves comparable or even better runtime performance to state-of-the-art frameworks on both single GPU and multi-GPU settings.
Distributionally Robust Recurrent Decoders with Random Network Distillation

Neural machine learning models can successfully model language that is similar to their training distribution, but they are highly susceptible to degradation under distribution shift, which occurs in many practical applications when processing out-of-domain (OOD) text.This has been attributed to "shortcut learning": relying on weak correlations over arbitrary large contexts.We propose a method based on OOD detection with Random Network Distillation to allow an autoregressive language model to automatically disregard OOD context during inference, smoothly transitioning towards a less expressive but more robust model as the data becomes more OOD, while retaining its full context capability when operating in-distribution.We apply our method to a GRU architecture, demonstrating improvements on multiple language modeling (LM) datasets.
IMPROVING THE STUDENTS’ VOCABULARY AND CREATIVITY BY DRAW STORY IN LEARNING ENGLISH

The aim of this study was to improve the students’ vocabulary and creativity using Draw Story in learning English. The method of this study was classroom action research which had three cycles whereby there were two meetings for each cycle. Each cycle consisted of four steps: planning, implementing, observation and reflection. The field notes and test constituted the research data. The qualitative data were analyzed through assembling the data, coding, comparing, building interpretations, and reporting the outcomes. The quantitative data were analyzed by comparing the mean scores of the pre-test and the post- tests. The research findings showed that draw story could improve: (1) the students’ vocabulary; (2) the students’ ability in identifying the use of appropriate words; (3) the students’ ability in spelling and pronouncing the words correctly; (4) the students’ creativity in drawing.
Implementing quantum Fourier transform using three qubits

Using the circulant symmetry of a Hamiltonian describing three qubits, we realize the quantum Fourier transform. This symmetry allows us to construct a set of eigenvectors independently on the magnitude of physical parameters involved in the Hamiltonian and as a result, the entanglement will be maintained. The realization will be leaned on trapped ions and the gate implementation requires an adiabatic transition from each spin product state to Fourier modes. The fidelity was numerically calculated and the results show important values. Finally, we discuss the acceleration of the gate by using the counter-driving field.
Automatic Guide Generation for Stan via NumPyro

Stan is a very popular probabilistic language with a state-of-the-art HMC sampler but it only offers a limited choice of algorithms for black-box variational inference. In this paper, we show that using our recently proposed compiler from Stan to Pyro, Stan users can easily try the set of algorithms implemented in Pyro for black-box variational inference. We evaluate our approach on PosteriorDB, a database of Stan models with corresponding data and reference posterior samples. Results show that the eight algorithms available in Pyro offer a range of possible compromises between complexity and accuracy. This paper illustrates that compiling Stan to another probabilistic language can be used to leverage new features for Stan users, and give access to a large set of examples for language developers who implement these new features.
Sanggar Seni Musik Tradisional Jawa Tengah Di Pengging - Boyolali

Pengging is a water tourism village area that combines historical tourism, cultural tourism, and nature tourism in one area. In addition to the water tour Pengging also has a high potential of traditional music art for the community. Although in Pengging there is an artist making musical instruments, but has not yet had a place that can be present in the development of the potential of the art. In Pengging not have a special building for the traditional music arts Gallery of Central Java to house the creativity of the artists and the community around. Workshops is a place of means for a community or group of people in conducting an event activity. This design aims to design the traditional music art Gallery of Central Java as a place to develop the potential of existing art. The methods used in literature studies, field observations, documentation and interviews are useful for finding out how many workshops are already in Pengging. The results obtained in the design can design a building that can educate and can provide all activities conducted in the workshop.
Domain Adaptation for Rare Classes Augmented with Synthetic Samples

To alleviate lower classification performance on rare classes in imbalanced datasets, a possible solution is to augment the underrepresented classes with synthetic samples. Domain adaptation can be incorporated in a classifier to decrease the domain discrepancy between real and synthetic samples. While domain adaptation is generally applied on completely synthetic source domains and real target domains, we explore how domain adaptation can be applied when only a single rare class is augmented with simulated samples. As a testbed, we use a camera trap animal dataset with a rare deer class, which is augmented with synthetic deer samples. We adapt existing domain adaptation methods to two new methods for the single rare class setting: DeerDANN, based on the Domain-Adversarial Neural Network (DANN), and DeerCORAL, based on deep correlation alignment (Deep CORAL) architectures. Experiments show that DeerDANN has the highest improvement in deer classification accuracy of 24.0% versus 22.4% improvement of DeerCORAL when compared to the baseline. Further, both methods require fewer than 10k synthetic samples, as used by the baseline, to achieve these higher accuracies. DeerCORAL requires the least number of synthetic samples (2k deer), followed by DeerDANN (8k deer).
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality

Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data. Standard imitation learning may learn a suboptimal policy from demonstrations with varying optimality. Prior works use confidence scores or rankings to capture beneficial information from demonstrations with varying optimality, but they suffer from many limitations, e.g., manually annotated confidence scores or high average optimality of demonstrations. In this paper, we propose a general framework to learn from demonstrations with varying optimality that jointly learns the confidence score and a well-performing policy. Our approach, Confidence-Aware Imitation Learning (CAIL) learns a well-performing policy from confidence-reweighted demonstrations, while using an outer loss to track the performance of our model and to learn the confidence. We provide theoretical guarantees on the convergence of CAIL and evaluate its performance in both simulated and real robot experiments. Our results show that CAIL significantly outperforms other imitation learning methods from demonstrations with varying optimality. We further show that even without access to any optimal demonstrations, CAIL can still learn a successful policy, and outperforms prior work.
Theory of Control Stochastic Systems with Unsolved Derivatives

Various types of stochastic differential systems with unsolved derivatives (SDS USD) arise in problems of analytical modeling and estimation (filtering, extrapolation, etc.) for control stochastic systems, when it is possible to neglect higher-order time derivatives. Methodological and algorithmic support of analytical modeling, filtering, and extrapolation for SDS USD is developed. The methodology is based on the reduction of SDS USD to SDS by means of linear and nonlinear regression models. Two examples that are illustrating stochastic aspects of methodology are presented. Special attention is paid to SDS USD with multiplicative (parametric) noises.
An Empirical Study of Online Learning in Non-stationary Data Streams Using Ensemble of Ensembles

Numerous information system applications produce a huge amount of non-stationary streaming data that demand real-time analytics.Classification of data streams engages supervised models to learn from a continuous infinite flow of labeled observations.The critical issue of such learning models is to handle dynamicity in data streams where the data instances undergo distributional change called concept drift.The online learning approach is essential to cater to learning in the streaming environment as the learning model is built and functional without the complete data for training in the beginning.Also, the ensemble learning method has proven to be successful in responding to evolving data streams.A multiple learner scheme boosts a single learner's prediction by integrating multiple base learners that outperform each independent learner.The proposed algorithm EoE (Ensemble of Ensembles) is an integration of ten seminal ensembles.It employs online learning with the majority voting to deal with the binary classification of non-stationary data streams.Utilizing the learning capabilities of individual sub ensembles and overcoming their limitations as an individual learner, the EoE makes a better prediction than that of its sub ensembles.The current communication empirically and statistically analyses the performance of the EoE on different figures of merits like accuracy, sensitivity, specificity, G-mean, precision, F1-measure, balanced accuracy, and overall performance measure when tested on a variety of real and synthetic datasets.The experimental results claim that the EoE algorithm outperforms its state-of-the-art independent sub ensembles in classifying non-stationary data streams.
Governance in the Era of Blockchain Technology in Qatar: A Roadmap and a Manual for Trade Finance

Nations worldwide have sought to capitalize on the benefits of distributed ledger technology (DLT) including Blockchain, but struggled to strike a balance between encouraging investment and innovation in the technology while addressing the challenges and uncertainties through regulation. Through its FinTech (Financial Technology) Strategy, Qatar has sought to embrace DLT, but its regulatory approach also remains cautious. Trade Finance is an ideal business process to be disrupted through the benefits of DLT and especially Blockchain technology, since its processes remain antiquated, inefficient and lack digitization. Blockchain as a form of DLT particularly offers the Trade Finance process not only more rapid, secure, cost-effective and efficient procedures, but importantly completely assures trust between importers and exporters and removes the requirement to place such trust in third-party intermediaries. Qatar can reap considerable economic benefits through the enhancement of its Trade Finance regulations enabling the adoption of such Blockchain technology. As such, the authors propose a roadmap and manual for the governance of the Trade Finance Blockchain ecosystem in Qatar. The authors propose multi-layered governance approach to the regulation of Blockchain in Qatar by (1) embracing international regulations and standards; (2) replicating foreign regional and national rules that are appropriate and innovative; and (3) applying sandbox regulations to Blockchain products and services.