Reviewerly/exHarmony huggingface dataset:
Interdisciplinary Approach to Modeling in Teaching English for Specific Purposes in the Ukrainian Context

The article highlights exploiting modeling in the interdisciplinary educational process at a modern technical university in Ukraine. The focus of the study is to consider the interdisciplinary approach to modeling in teaching English for Specific Purposes (ESP) to develop technical students' strategic competence as the cognitive ability to intervene in solving various problems. The methodology involves the complex of complementary methods: analysis of psychological and pedagogical sources on the issue under study; the observation of the educational process, and monographic practice (interpreting the results obtained in a coherent, logical perspective); generalization and systematization of the collaboration between the Faculty of Chemical Engineering and the Faculty of Linguistics of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" (after this officially abbreviated name – Igor Sikorsky KPI) on implementing the model of interdisciplinary interaction to develop students' strategic competence through producing secondary sci-tech texts in the course of preparing their qualifying papers. The suggested interdisciplinary model covers three stages of realization: initial stage (activates knowledge in the subject area transferred to the foreign language environment), cognitive-communicative stage (activates the cognitive processes to develop the levels of thinking within the framework of Bloom's taxonomy), and communicative-productive stage (develops communicative strategies for writing abstracts of patent documents in English).
Who 'Owns' the Data in a Coronavirus Tracing (And/or Tracking) App?

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Learning of discrete graphical models with neural networks

Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a discrete graphical model given i.i.d samples from its joint distribution can be solved with near-optimal sample complexity using a convex optimization method known as Generalized Regularized Interaction Screening Estimator (GRISE). But the computational cost of GRISE becomes prohibitive when the energy function of the true graphical model has higher order terms. We introduce NeurISE, a neural net based algorithm for graphical model learning, to tackle this limitation of GRISE. We use neural nets as function approximators in an Interaction Screening objective function. The optimization of this objective then produces a neural-net representation for the conditionals of the graphical model. NeurISE algorithm is seen to be a better alternative to GRISE when the energy function of the true model has a high order with a high degree of symmetry. In these cases NeurISE is able to find the correct parsimonious representation for the conditionals without being fed any prior information about the true model. NeurISE can also be used to learn the underlying structure of the true model with some simple modifications to its training procedure. In addition, we also show a variant of NeurISE that can be used to learn a neural net representation for the full energy function of the true model. Â© 2020 Neural information processing systems foundation. All rights reserved.
Stationary Distribution Markov Chain for COVID-19 Pandemic

Coronavirus disease (COVID-19) is a new disease found in the late 2019. The first case was reported on December 31, 2019 in Wuhan, China and spreading all over the countries. The disease was quickly spread to all over the countries. There are 206.900 cases confirmed by March 18, 2020 causing 8.272 death. It was predicted that the number of confirmed cases will continue to increase. On January 30, 2020, WHO declared this as pandemic for the 6th time ever since the swine influenza. There are a lot of researchers which discuss pandemic spreading caused by virus with mathematical modelling. In this paper, we discuss a long-term prediction over the COVID-19 spreading using stationary distribution markov chain. The goal is to analyze the prediction of infected people in long-term by analyzing the COVID-19 daily cases in an observation interval. By analyzing the daily cases of COVID-19 in Indonesia from March 2nd, 2020 until November 1st, 2020, result shown that 53.91% of probability that the COVID-19 daily case will incline in long-term, 44.86% of chance will decline, and 1.23% of chance will stagnant.
Firefly Algorithm Based on Dynamic Step Change Strategy

Firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, like other intelligent algorithms, the firefly algorithm still has some shortcomings, such as the algorithm is easy to fall into the local optimal, and the convergence speed is slow in the later period. Therefore, this paper proposes a new firefly algorithm with dynamic step change strategy (DsFA) to balance the global and local search capabilities. Thirteen well-known benchmark functions are used to verify the performance of our proposed method, the computational results show that DsFA is more efficient than many other FA algorithms.
Scalable Resilience Against Node Failures for Communication-Hiding Preconditioned Conjugate Gradient and Conjugate Residual Methods.

The observed and expected continued growth in the number of nodes in large-scale parallel computers gives rise to two major challenges: global communication operations are becoming major bottlenecks due to their limited scalability, and the likelihood of node failures is increasing. We study an approach for addressing these challenges in the context of solving large sparse linear systems. In particular, we focus on the pipelined preconditioned conjugate gradient (PPCG) method, which has been shown to successfully deal with the first of these challenges. In this paper, we address the second challenge. We present extensions to the PPCG solver and two of its variants which make them resilient against the failure of a compute node while fully preserving their communication-hiding properties and thus their scalability. The basic idea is to efficiently communicate a few redundant copies of local vector elements to neighboring nodes with very little overhead. In case a node fails, these redundant copies are gathered at a replacement node, which can then accurately reconstruct the lost parts of the solver's state. After that, the parallel solver can continue as in the failure-free scenario. Experimental evaluations of our approach illustrate on average very low runtime overheads compared to the standard non-resilient algorithms. This shows that scalable algorithmic resilience can be achieved at low extra cost.
Unfolding recurrence by Green’s functions for optimized reservoir computing

Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics. This sets them apart from deep feed-forward networks. Despite the tremendous progress in the application of feed-forward networks and their theoretical understanding, it remains unclear how the interplay of recurrence and non-linearities in recurrent cortical networks contributes to their function. The purpose of this work is to present a solvable recurrent network model that links to feed forward networks. By perturbative methods we transform the time-continuous, recurrent dynamics into an effective feed-forward structure of linear and non-linear temporal kernels. The resulting analytical expressions allow us to build optimal time-series classifiers from random reservoir networks. Firstly, this allows us to optimize not only the readout vectors, but also the input projection, demonstrating a strong potential performance gain. Secondly, the analysis exposes how the second order stimulus statistics is a crucial element that interacts with the non-linearity of the dynamics and boosts performance.
Learning Disentangled Representations of Videos with Missing Data

Missing data poses significant challenges while learning representations of video sequences. We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that imputes and predicts future video frames in the presence of missing data. Specifically, DIVE introduces a missingness latent variable, disentangles the hidden video representations into static and dynamic appearance, pose, and missingness factors for each object. DIVE imputes each object's trajectory where data is missing. On a moving MNIST dataset with various missing scenarios, DIVE outperforms the state of the art baselines by a substantial margin. We also present comparisons for real-world MOTSChallenge pedestrian dataset, which demonstrates the practical value of our method in a more realistic setting. Our code and data can be found at this https URL.
Depth Ranging Performance Evaluation and Improvement for RGB-D Cameras on Field-Based High-Throughput Phenotyping Robots

RGB-D cameras have been successfully used for indoor High-ThroughPut Phenotyping (HTPP). However, their capability and feasibility for in-field HTPP applications still need to be evaluated. To solve the problem, we evaluate the depth-ranging performances of a consumer-level RGB-D camera (RealSense D435i) under in-field scenarios. First, we focus on determining their optimal ranging areas for different crop organs. Second, based on the evaluation results, we analyze the influences of light intensity on depth measurements and propose a brightness-and-distance based Support Vector Regression Strategy, to compensate the ranging error. Finally, we give an intuitive accuracy ranking diagram for RealSense D435i under natural lighting intensities. Experimental results show that: 1) RealSense D435i has good ranging performances on in-field HTPP. 2) Our error compensation model can effectively reduce the influences of lighting intensity and target distance.
Realtime Single-Shot Refinement Neural Network for 3D Obejct Detection from LiDAR Point Cloud

3D object detection from point cloud is an important aspect of environmental perception in intelligent systems such as autonomous driving systems and robot systems. However, efficient 3D feature extraction and accurate object localization is challenging for current algorithms. In this paper, we introduce a new single-shot refinement neural network for fast and accurate 3D object detection. Firstly, we simplify the 3D feature extraction network and use single-shot object detector to increase processing speed. Secondly, we exploit self-attention mechanism in main object detection branch to improve object feature representation. Thirdly, an object refinement branch is introduced to produce a finer regression of objects upon the primary estimation from the main detection branch. Both modifications lead to further improvements in performance without additional computational cost. Our approach is tested on KITTI 3D Car detection benchmark and achieves good results in the validation set. The running speed is around 40 frame per second.
Pelatihan Pengelolaan Keuangan Berbasis Komputer Dalam Peningkatan Tertib Administrasi Pada Kader Kesehatan Di Giwangan Umbulharjo Yogyakarta

PLKB health cadres in Giwangan Yogyakarta are now using conventional methods in accounting start from managing administrative data to financial processing. It requires manual documentation by writing on sheets of paper. Data loss due to paper based is vulnerable Apart from the possibility of unclear writing, tearing can cause damage on this document and cause problems that make it difficult for accountability. This paper applies the training method by giving lectures and computer demonstrations. The level of participant education requires a patient speaker in delivering the material in slowly and detail. Hence, the constraints required good, polite treatment, speech andwell ethics. The results of activity to the PLKB health cadre in Giwangan are expected to increase the understanding and give insight of computers beneficial in financial management. Besides, this activity can increase motivation in computerimplementation optimallyKeywords: PLKB cadres, financial management, training
Classification Heart Diseases Base on Heart Sound Using Backpropagation Algorithm

Heart sounds have a special pattern that can indicate a person's heart condition. An abnormal heart will produce a characteristic sound that is often called a murmur ( Sitinjak , 2008). Murmurs are caused by various things that can indicate a person's heart condition. From these murmurs can be known the types of abnormalities experienced by patients. In this study, cardiac abnormalities that can be identified are aortic stenosis (as), mitral regurgitation (mr), mitral valve prolapse (mvp), mitral stenosis (ms), and normal. The data used for training as many as 1000 heart sound files consisting of 200 files each for each heart abnormality.Data in the form of heart rate sound samples with the format. Wav. The program was created using the Artificial Neural Network method to identify the five types of cardiac abnormalities. The training method is created using the traingdx function provided in the Neural Network Toolbox on MATLAB. Based on the results of the training can be obtained a validity value of 97,7%.
Adverse Consequences of Virtual Education and Virtual Learning

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Coding Textual Inputs Boosts the Accuracy of Neural Networks

Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs.We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as inputs.As "alternatives" to a text representation, we introduce Soundex, MetaPhone, NYSIIS, logogram to NLP, and develop fixed-output-length coding and its extension using Huffman coding.Each of those codings combines different character/digital sequences and constructs a new vocabulary based on codewords.We find that the integration of those codewords with text provides more reliable inputs to Neural-Networkbased NLP systems through redundancy than text-alone inputs.Experiments demonstrate that our approach outperforms the state-ofthe-art models on the application of machine translation, language modeling, and part-ofspeech tagging.The source code is available at https://github.com/abdulrafae/codingnmt.
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes

We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome. These explanations describe the outcome an agent is trying to achieve by its actions. We provide a simple proof that general methods for post-hoc explanations of this nature are impossible in traditional reinforcement learning. Rather, the information needed for the explanations must be collected in conjunction with training the agent. We derive approaches designed to extract local explanations based on intention for several variants of Q-function approximation and prove consistency between the explanations and the Q-values learned. We demonstrate our method on multiple reinforcement learning problems, and provide code to help researchers introspecting their RL environments and algorithms.
Adaptive Median Filter Based Noise Removal Algorithm for Big Image Data

In this work, develop and evaluate a novel Adaptive Median Filter (AMF) for noise removal which is devoted in the direction of contribution a consistent and proficient model for increasing the image quality in the existence of high noise levels. AMF algorithm is proposed for noise removal in the direction of increases quality of the image by reducing noises in the images. Filtering methods such as Decision Based Filtering (DBF), Median Filter (MF) and AMF are evaluated in terms of their force on the following image analysis. The experimental results show with the purpose of all of these filtering methods is able to give better results and higher value as the amount of noise being removed increases. The filtering methods results are measured in terms of metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and time efficiency. The results analysis demonstrates that the proposed AMF algorithm is reliable for image data sets.
Finite-Time Analysis for Double Q-learning

Although Q-learning is one of the most successful algorithms for finding the best action-value function (and thus the optimal policy) in reinforcement learning, its implementation often suffers from large overestimation of Q-function values incurred by random sampling. The double Q-learning algorithm proposed in~\citet{hasselt2010double} overcomes such an overestimation issue by randomly switching the update between two Q-estimators, and has thus gained significant popularity in practice. However, the theoretical understanding of double Q-learning is rather limited. So far only the asymptotic convergence has been established, which does not characterize how fast the algorithm converges. In this paper, we provide the first non-asymptotic (i.e., finite-time) analysis for double Q-learning. We show that both synchronous and asynchronous double Q-learning are guaranteed to converge to an $\epsilon$-accurate neighborhood of the global optimum by taking $\tilde{\Omega}\left(\left( \frac{1}{(1-\gamma)^6\epsilon^2}\right)^{\frac{1}{\omega}} +\left(\frac{1}{1-\gamma}\right)^{\frac{1}{1-\omega}}\right)$ iterations, where $\omega\in(0,1)$ is the decay parameter of the learning rate, and $\gamma$ is the discount factor. Our analysis develops novel techniques to derive finite-time bounds on the difference between two inter-connected stochastic processes, which is new to the literature of stochastic approximation.
Minkowski products of unit quaternion sets

The Minkowski product of unit quaternion sets is introduced and analyzed, motivated by the desire to characterize the overall variation of compounded spatial rotations that result from individual rotations subject to known uncertainties in their rotation axes and angles. For a special type of unit quaternion set, the spherical caps of the 3-sphere $S^3$ in $\mathbb{R}^4$, closure under the Minkowski product is achieved. Products of sets characterized by fixing either the rotation axis or rotation angle, and allowing the other to vary over a given domain, are also analyzed. Two methods for visualizing unit quaternion sets and their Minkowski products in $\mathbb{R}^3$ are also discussed, based on stereographic projection and the Lie algebra formulation. Finally, some general principles for identifying Minkowski product boundary points are discussed in the case of full-dimension set operands.
Survey of nonlinear state estimation in aerospace systems with Gaussian priors

Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.
Core influence mechanism on vertex-cover problem through leaf-removal-core breaking

Leaf-Removal process has been widely researched and applied in many mathematical and physical fields to help understand the complex systems, and a lot of problems including the minimal vertex-cover are deeply related to this process and the Leaf-Removal cores. In this paper, based on the structural features of the Leaf-Removal cores, a method named Core Influence is proposed to break the graphs into No-Leaf-Removal-Core ones, which takes advantages of identifying some significant nodes by localized and greedy strategy. By decomposing the minimal vertex-cover problem into the Leaf-Removal cores breaking process and maximal matching of the remained graphs, it is proved that any minimal vertex-covers of the whole graph can be located into these two processes, of which the latter one is a P problem, and the best boundary is achieved at the transition point. Compared with other node importance indices, the Core Influence method could break down the Leaf-Removal cores much faster and get the no-core graphs by removing fewer nodes from the graphs. Also, the vertex-cover numbers resulted from this method are lower than existing node importance measurements, and compared with the exact minimal vertex-cover numbers, this method performs appropriate accuracy and stability at different scales. This research provides a new localized greedy strategy to break the hard Leaf-Removal Cores efficiently and heuristic methods could be constructed to help understand some NP problems.
Impact of learning, exclusively online, during a pandemic

This study focuses on the use of a distance learning platform to deliver on-line classes, during the second semester of the 2019-2020 academic year, in a higher school in an urban area. The study is carried out mainly with, the aim of understanding on the perception and admissibility or not of distant teaching, in its entirety, by students accustomed to on-site teaching. The data were collected by the course activities reports and a questionnaire survey carried out among the 207 students registered on a distance learning platform; the results obtained allow to deduce the interest of the learning environment and of the interactions between actors during teaching practices using technological tools in a distant teaching process.
Teacher professional development based on the DigCompEdu framework

The European Framework for the Digital Competence of Educators (DigCompEdu) describes in six competence areas, which digital competences educators need specific to their profession in order to be able to integrate digital technologies in education By referring to DigCompEdu we examine how a teacher professional development (TPD) course should be designed to promote them Current research results on effective TPD courses, were used as a basis for the design of TPD courses to promote DigCompEdu competences In three iterations the TPD concept was analysed, conducted and further developed The last iteration of the execution was paused due to Covid-19 Preliminary results suggest that the TPD supports teachers in improving their digital competence A final analysis of the data is expected in early 2021 © ICCE 2020 - 28th International Conference on Computers in Education, Proceedings All rights reserved