Reviewerly/exHarmony huggingface dataset:
PEMBERDAYAAN GURU DALAM IMPLEMENTASI PEMBELAJARAN ABAD 21

This form of devotion training aims to provide detailed guidance on designing and developing a 21st century learning in the form of e-learning. Train and design new instructors in the e-learning design orientation and provide basic concepts and information about the processes and resources involved in e-learning. Designing and managing e-learning is a new skill in the 21st century that teachers must have. The method of activities used are training, mentoring, implementation, and evaluation. Participants in the training and mentoring activities were junior high school science teachers from Cirebon, Cirebon, and Indramayu districts. Data in the form of observations and questionnaires were analyzed descriptively. The results of training and mentoring that teachers are able to use e-learning well. Teachers can create a learning space in e-learning that contains student teaching materials. The application of e-learning in each school still has limited facilities and infrastructure. The results of the questionnaire responses showed a positive response to the 21st century training and service activities.
Deep Message Passing on Sets

Modern methods for learning over graph input data have shown the fruitfulness of accounting for relationships among elements in a collection. However, most methods that learn over set input data use only rudimentary approaches to exploit intra-collection relationships. In this work we introduce Deep Message Passing on Sets (DMPS), a novel method that incorporates relational learning for sets. DMPS not only connects learning on graphs with learning on sets via deep kernel learning, but it also bridges message passing on sets and traditional diffusion dynamics commonly used in denoising models. Based on these connections, we develop two new blocks for relational learning on sets: the set-denoising block and the set-residual block. The former is motivated by the connection between message passing on general graphs and diffusion-based denoising models, whereas the latter is inspired by the well-known residual network. In addition to demonstrating the interpretability of our model by learning the true underlying relational structure experimentally, we also show the effectiveness of our approach on both synthetic and real-world datasets by achieving results that are competitive with or outperform the state-of-the-art. For readers who are interested in the detailed derivations of serveral results that we present in this work, please see the supplementary material at: https://arxiv.org/abs/1909.09877.
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based methods struggle to achieve the same asymptotic performance as model-free methods. In this paper, We propose a Policy Optimization method with Model-Based Uncertainty (POMBU)—a novel model-based approach—that can effectively improve the asymptotic performance using the uncertainty in Q-values. We derive an upper bound of the uncertainty, based on which we can approximate the uncertainty accurately and efficiently for model-based methods. We further propose an uncertainty-aware policy optimization algorithm that optimizes the policy conservatively to encourage performance improvement with high probability. This can significantly alleviate the overfitting of policy to inaccurate models. Experiments show POMBU can outperform existing state-of-the-art policy optimization algorithms in terms of sample efficiency and asymptotic performance. Moreover, the experiments demonstrate the excellent robustness of POMBU compared to previous model-based approaches.
Identification of Abnormal Processes with Spatial-Temporal Data Using Convolutional Neural Networks

Identifying abnormal process operation with spatial-temporal data remains an important and challenging work in many practical situations. Although spatial-temporal data identification has been extensively studied in some domains, such as public health, geological condition, and environment pollution, the challenge associated with designing accurate and convenient recognition schemes is very rarely addressed in modern manufacturing processes. This paper proposes a general recognition framework for identifying abnormal process with spatial-temporal data by employing a convolutional neural network (CNN) model. Firstly, motivated by the pasting case study, the spatial-temporal data are transformed into process images for capturing spatial and temporal interrelationship. Then, the CNN recognition model is presented for identifying different types of these process images, leading to the identification of abnormal process with spatial-temporal data. The specific architecture parameters of CNN are determined step by step. According to the performance comparison with alternative methods, the proposed method is able to accurately identify the abnormal process with spatial-temporal data.
A New Burrows Wheeler Transform Markov Distance

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems. We describe issues with this approach that were not widely known, and introduce our new Burrows Wheeler Markov Distance (BWMD) as an alternative. The BWMD avoids the shortcomings of earlier efforts, and allows us to tackle problems in variable length DNA sequence clustering. BWMD is also more adaptable to other domains, which we demonstrate on malware classification tasks. Unlike other compression-based distance metrics known to us, BWMD works by embedding sequences into a fixed-length feature vector. This allows us to provide significantly improved clustering performance on larger malware corpora, a weakness of prior methods.
Design of a 360-deg panoramic capture system based on a smart phone

A panoramic imaging system has been developed using two afocal lenses and a smart phone that consists of cameras on both sides. An individual afocal lens has been developed with a large field of view (FOV) and a large exit pupil, which can enlarge the FOV of a smart phone camera to above 180 deg. Some issues with the smart phone-based 360-deg panoramic system, such as relative illuminance and assembly tolerance, were analyzed in detail and taken into consideration during the design procedure. A prototype has been developed with a low fabrication cost, yet producing impressive panoramic image quality.
Performance enhancement of the internet of things with the integrated blockchain technology using RSK sidechain

Blockchain technology was first built as the framework underlying crypto-currency; it has now shown immense potentials with far-reaching implications in the arena of smart contract-based financial markets, bitcoin integrated artificial intelligence and mostly the distributed ledgeroriented security mechanism of the IoT [1].The technology lets end-users to communicate and record the value and information called transaction on a peer-to-peer network of computers and smart devices.
Generating Interactive Worlds with Text

Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.
METEOR DETECTION USING DEEP CONVOLUTIONAL NEURAL NETWORKS

This paper describes an application of a pre-trained Deep Convolutional Neural Network to detect meteors from night sky images.The dataset is relatively small, composed of labeled images of meteors and non-meteors from the night sky.Techniques like data augmentation were used to create data artificially, and a dropout layer was introduced to prevent overfitting at the training augmented dataset.The performance obtained by the methods using traditional five-fold cross-validation along with changing of the images for black and white, compared with the ones obtained using only the training and validation partitions and RGB colors, achieved a higher mean accuracy of 84.35%.Resumo: Este artigo descreve a aplicação de uma rede neural convolucional profunda prétreinada para detectar meteoros a partir de imagens do céu noturno.O conjunto de dados é relativamente pequeno, composto de imagens rotuladas de meteoros e não-meteoros.Técnicas como aumento de dados foram usadas para criar dados artificialmente, e uma camada de dropout foi introduzida para evitar overfitting no dataset aumentado para treinamento.O desempenho obtido pelos métodos usando a validação cruzada com cinco partições, juntamente com a mudança das imagens para preto e branco, em comparação com os obtidos usando apenas as partições de treinamento e validação e cores RGB, alcançou
Wavelet-Based Self-adaptive Hierarchical Thresholding Algorithm and Its Application in Image Denoising

This paper attempts to construct a suitable wavelet for image denoising based on wavelet thresholding algorithm.First, the author discussed how image thresholding is affected by the wavelet orthogonality and bi-orthogonality, the features of vanishing moments and the odd or even symmetry of the decomposition end filter.The discussion shows that the most desirable wavelet for image denoising is the biorthogonal wavelet, in which the decomposition end filter has zero point even symmetry, the low-pass decomposition enjoys a wide support interval, and the high-pass decomposition filter has a short support and attenuates fast.On this basis, three zero point even symmetric biorthogonal wavelets with different vanishing moment features were developed through the parametric construction of fixed-length tightly-supported (FLTS) biorthogonal wavelet, and a self-adaptive hierarchical thresholding algorithm was designed.The simulation results show that the developed wavelets have excellent denoising ability and enhance the images with rich details.Coupled with the self-adaptive hierarchical thresholding algorithm, these wavelets can effectively improve the image quality.
The Effect of Interactive Board Games (IBG) on Vocabulary Achievement

Vocabulary Achievement is an element of learning the language which may assist students in understanding the language through basic English skills. Interactive Board Games are board games that use multimedia system (PowerPoint) teaching technique. This is often one of the traditional games that may be played by group activity and paired with a blackboard to focus on visual media activity. To find out the effect of using Interactive Board Games on students’ vocabulary achievement, we used the quantitative approach by assigning experimental research. There were two groups that we used in this research. Group one as experimental group and other as a control group. We used Total Pscycal Response (TPR) method for both groups, but we often gave Interactive Board Games to the experimental group in the learning process. There were eight meetings that we conducted into the experimental group with treatment using Interactive Board Games. We calculated the data from the test given when students learning English used Interactive Board Games. Before the test gave to the sample, the writer gave the test to students who have the same ability but not sample to know regarding validity and reliable of the test. Based on the t-test, we found that the t-ratio was higher than the t-table. The result indicated that there was a significant difference between the students who were taught using Interactive Board Games and the students who were not taught using Interactive Board Games.
ANALISA LIMPASAN BERDASARKAN CURAH HUJAN MENGGUNAKAN MODEL ARTIFICAL NEURAL NETWORK (ANN) DI SUB DAS BRANTAS HULU

Discharge data is usually less available than rainfall data, so it is necessary to find a relationship between river flows that are applied in the period available rainfall data in a watershed area. The purpose of this study is to determine the suitability of the method based on the analysis of data validation between the observed discharge and the model discharge. The method is done by modeling the discharge based on rainfall with the Artificial Neural Network (ANN) MATLAB R2014b program. The Upper Brantas Watershed is used as a case study because it often has runoff problems. Validation of the ANN method was tested with Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (R) and Relative Error (KR). From the results of calibration using the ANN Model, the best data is found in the five years data of epoch 500. Verification results based on the value of R have a relatively good relationship between observation discharges with model discharges. The validation results show the validity in a year data of epoch 500.
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings

The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) perform a comprehensive comparison of state-of-the-art multilingual word and sentence encoders on the tasks of named entity recognition (NER) and part of speech (POS) tagging; and (ii) propose a new method for creating multilingual contextualized word embeddings, compare it to multiple baselines and show that it performs at or above state-of-the-art level in zero-shot transfer settings. Finally, we show that our method allows for better knowledge sharing across languages in a joint training setting.
GDFace: Gated Deformation for Multi-View Face Image Synthesis

Photorealistic multi-view face synthesis from a single image is an important but challenging problem. Existing methods mainly learn a texture mapping model from the source face to the target face. However, they fail to consider the internal deformation caused by the change of poses, leading to the unsatisfactory synthesized results for large pose variations. In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. Specifically, we propose a dual network that consists of two modules. The first module estimates the deformation of two views in the form of convolution offsets according to the input and target poses. The second one, on the other hand, leverages the predicted deformation offsets to create the target face image. In this way, pose changes are explicitly modeled in the face generator to cope with geometric transformation, by adaptively focusing on pertinent regions of the source image. To compensate offset estimation errors, we introduce a soft-gating mechanism that enables adaptive fusion between deformable features and primitive features. Extensive experimental results on five widely-used benchmarks show that our approach performs favorably against the state-of-the-arts on multi-view face synthesis, especially for large pose changes.
ACG-Engine: An Inference Accelerator for Content Generative Neural Networks

The technological breakthrough in Generative Adversarial Networks (GAN) has propelled the advancement of content generative applications such as AI-based paintings, style transfer, and music composition. However, in contrast to previous deep learning models for prediction and categorization, generative networks generally rely on instance normalization (IN) layer for better feature distribution, which performs significantly better than batch normalization(BN) in image style-transfer, image to image translation, etc. Unlike batch or group normalization that can be fused into convolutional layers and ignored during the network inference stage, an instance normalization layer induces intensive computation and memory access. However, prior deep learning accelerator designs for traditional Neural Network and Generative Adversarial Networks mostly focus on the acceleration of convolution and deconvolution layer but lack of support for IN operations, which could become a performance bottleneck on edge devices with insufficient computational power. To address this problem, we propose an inference accelerator for content generation (ACG-Engine) aimed to support the fundamental operations of generative networks, including convolution layers, deconvolution layers, specifically instance normalization layer. We performed a hardware-aware mathematical transformation of the IN operation for less computation complexity and memory-friendliness, so that it can be efficiently mapped to the classic 2D processing element array. Owing to the proposed optimization techniques, ACG-Engine achieves 4.56X speedup and improve power efficiency up to 29X compared to prior baseline acceleration scheme in generative network acceleration. In addition, ACG-Engine can achieve performance comparable to the classic CNN-specific accelerators with negligible power consumption and area overhead.
Variable selection in finite mixture of regression models using the skew-normal distribution

Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression error. Such assumptions are unsuitable for a set of data containing a group or groups of observations with asymmetric behavior. In this paper, we introduce a variable selection procedure for FMR models using the skew-normal distribution. With appropriate choice of the tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. To estimate the parameters of the model, a modified EM algorithm for numerical computations is developed. The methodology is illustrated through numerical experiments and a real data example.
Image edge detection techniques

Sudden changes of discontinuities are known as the edge of the image.Basically, three types of edges in an image, they are named vertical, diagonal and horizontal edge.Normally shape of an image is enclosed with the edges.Detection of an image is very important then there will be required of sharpness to be increased for further analysis.Here simple technique like canny, Robert, Sobel, Prewitt and Laplacian operator are analyzed.To extract significant data from the image is the best relevant method or technique is an image processing like image enhancement, image acquisition or image segmentation.For accurate result image conversion, image filtering, histogram analysis, edge detectionbased segmentation and lastly threshold-based segmentation required [1].Sharp discontinuities in an image can be found out by using Edge detection technique for identifying and locating an exact position [2].Now a days, so many new industrial tools which acquire images a lot and became handy to an ample number of users [3].
A Method of Trajectory Planning for Unmanned Aerial Vehicle Formation Based on Fluid Dynamic Model

This paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formations. A hybrid trajectory planning algorithm based on potential field fluid dynamic model and bidirectional fast search random tree is proposed to improve the ability of UAV formation to adapt to complex dynamic environment. Firstly, a dynamic system mathematical model based on fluid potential energy field is proposed; and the obstacle potential energy function and potential energy function between the formations modify the disturbance flow field. Secondly, IBi-directional Rapidly Exploring Random Tree (IBi-RRT) algorithm with adaptive step size is scheduled to solve the dispersive and convergent streamlines of disturbed flow field and to plan the trajectory. This method can clarify the flow field streamlines by adaptive step size combined with rolling detection method, which greatly improves the formation's ability to avoid dynamic threats. The experimental results show that the proposed improved fluid potential energy field dynamic system and IBi-RRT hybrid trajectory planning algorithm with adaptive step size can effectively improve the adaptive ability of UAV formation to the dynamic environment, and can plan the ideal trajectory in response to unexpected situations.
Implementation of Rabin Karp Algorithm for Essay Writing Test System on Organization xyz

Every year the xyz organization conducts written tests for participants who will be administrators of the xyz organization, written tests are chosen because participants can convey information verbally. But it is constrained for correction, because to correct essay answers manually will require a lot of time, while the answers from participants 1 and other participants are different. By kaera the written test system owned by the xyz organization needs to be developed to facilitate correction of participants' answers to the system, so that the assessment process can be done easily and quickly. To correct answers quickly and accurately, a system is needed that can correct the answers to essays by applying the Rabin Karp algorithm. Rabin Karp is a very efficient multiple pattern search algorithm that searches for many pattern sets using the hash method in searching for words. The results of this system are expected to improve the answers to essay questions more easily and accurately. This theory is rarely used to find one word, but it is very important and very effective when used to look for several patterns. For this reason, in this study Rabin Karp's ability to detect similarities in the similarity of the participants' answers to the system will be proven by giving a similarity to the sentence or word. The process of equating sentences or words through preprocessing stages, so that the data becomes "clean", so it is feasible to do the hashing process. After hashing, the Rabin Karp algorithm is applied for each word and its similarity measurement uses the dice coefficient, which results in the same sentence value from the participant's answer and the system. The better the stemming process in the preprocessing process, the higher the value of the similarity.