Reviewerly/exHarmony huggingface dataset:
Fitting Gamma Mixture Density Networks with Expectation-Maximization Algorithm

We discuss how mixtures of Gamma distributions with mixing probabilities, shape and rate parameters depending on features can be fitted with neural networks. We develop two versions of the EM algorithm for fitting so-called Gamma Mixture Density Networks, which we call the EM network boosting algorithm and the EM forward network algorithm, and we test their implementation together with the choices of hyperparameters. A simulation study shows that our algorithms perform very well on synthetic data sets. We further illustrate the application of the Gamma Mixture Density Network on a real data set of motor insurance claim amounts and conclude that Gamma Mixture Density Networks can improve the fit of the regression model and the predictions of the claim severities used for rate-making compared to classical actuarial techniques.
Generative approach for 1D barcode dataset population for mobile-based recognition

Barcode recognition via the mobile device camera is an actual problem which occurs in various fields. To receive experimental baselines and provide methods comparison researchers require some carefully annotated data samples. For those purposes, some datasets have already been collected and published in the public domain. But they suffer from different disadvantages. In this paper, we present a novel challenging dataset designed for barcode reading quality evaluation. It is populated using the generative approach with well-established image augmentation technique application. Among them, different geometrical transformations, kind of noises, brightness and lighting variance were used. The generative approach also allowed to populate ground-truth automatically and exclude errors introduced during the manual process. The dataset consists of training (41184 images) and validation parts (10296 images). It contains seven most popular symbologies: CODABAR, CODE-39, CODE-93, CODE-128, EAN-13, UPC-A, UPC-E. As an experimental baseline, the reading quality obtained with the open-source Zxing library is provided. The dataset with all the supplementary materials is available at ftp://smartengines.com/barcode.
UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian

This paper describes the UdS-DFKI submission to the shared task for unsupervised machine translation (MT) and very low-resource supervised MT between German (de) and Upper Sorbian (hsb) at the Fifth Conference of Machine Translation (WMT20). We submit systems for both the supervised and unsupervised tracks. Apart from various experimental approaches like bitext mining, model pre-training, and iterative back-translation, we employ a factored machine translation approach on a small BPE vocabulary.
PENGGUNAAN MODEL PEMBELAJARAN SCRAMBLE UNTUK MENINGKATKAN MOTIVASI DAN PRESTASI BELAJAR PKn MATERI MENGENAL BENTUK-BENTUK KEPUTUSAN BERSAMA PADA SISWA KELAS V SEMESTER II SD 1 REJOSARI TAHUN PELAJARAN 2017/2018

This study aims to increase the motivation and learning achievement of students in grade V SD 1 Rejosari semester II of the 2017/2018 academic year in the Civics subject, the subject of recognizing the forms of collective decision To achieve this goal, this study was designed in two cycles. The procedure in each cycle includes the following steps: 1) planning, 2) implementation, 3) observation, 4) reflection. The effectiveness of actions in each cycle is measured from the results of the observations described, interpreted, then reflected to determine corrective actions in the next cycle. The action research carried out in two cycles resulted in the results in cycle I, the results of the observation of motivation reached a score of 3.5 with a value of 70 with a sufficient predicate and on the learning achievement results of 33 students there were 22 students or 66% achieved predetermined completeness while the rest 11 students or 34% have not yet achieved completeness. The highest score obtained by students is 100 while the lowest score is 60, the average student learning outcomes is 75. Data in cycle II on the results of observations of motivation reaches a score of 4.1 with a value of 82 with good predicate and the results of learning achievement show that there are 33 students. 32 students or 96% have achieved the predetermined completeness while the remaining 1 child or 4% have not yet reached the completeness. The highest score obtained by students is 100 while the lowest score is 70. Based on the actions taken, it can be concluded that the application of the scramble learning model can increase the motivation and achievement of the fifth grade students of SD 1 Rejosari semester II of the 2017/2018 academic year in the subject of knowing the forms of collective decision. With an increase in student motivation to learn will have an effect on increasing student achievement.
Research on UAV Signal Recognition Technology Based on GRU Neural Network Model

With the rapid development of the drone market, the drone black flight incident has become the norm. In order to be able to take countermeasures against black flying drones in a more targeted manner and accurately identify the models of black flying drones, the existing identification technology cannot identify the types of unmanned aerial vehicles in real time and accurately. This paper proposes the UAV signal recognition technology based on the GRU neural network model. Taking the rotor UAV as the research object, the GRU neural network model is used to extract the characteristics and model design of the UAV communication signal according to the characteristics of the UAV communication signal., Model practice and training, and analyze the test results of the UAV recognition technology designed in this article. Experimental results show that the technology can identify common UAV models more accurately.
Classification of Ultrasound Thyroid Nodule Images into Benign and Malignant Lesions

Thyroid cancer is generally diagnosed by visual inspection of ultrasound images. The proposed method is to automate the classification of thyroid nodules into cancerous or not. This is to assist radiologists. Value of Peak Signal to Noise Ratio (PSNR) for noise filters were compared. The Gaussian filter had the highest PSNR value and was applied to segmented images. Features of Elliptical fit, histogram, morphological, Gray Level Run Length Matrix (GLRLM) and Gray Level Co-occurrence Matrix (GLCM) are extracted from the pre-processed ultrasound images. F-score and Sequential Forward Feature Selection (SFFS) methods were used for feature selection. The optimal Cost function (C) and gamma parameters were obtained using the Grid Search algorithm. These are then used for the training the classifiers. Support Vector Machine (SVM) classifier and Linear Discriminant Analysis (LDA) performance was noted for both the feature selection methods and compared. An accuracy of 0.98 and 1.0 was obtained using LDA and SVM respectively with SFFS. An accuracy of 0.95 was obtained for both LDA and SVM with F-score.
A Similarity Measure for Text Document Using Term Cardinality

Abstract With enormous development of digital technology, data is being generated at rapid rate with various application domains. Data has to be extracted or filtered to find useful information. A basic concept for these tasks and applications are the distance measures to effectively determine how similar two objects are. In this paper, a novel similarity measure for clustering text documents is proposed using the cardinality of the terms in the documents. The bench mark algorithm k-medoids is used for clustering task. The results obtained from the proposed distance measure are compared with other standard distance measures like Manhattan, Euclidean distance measure. Dunn Index is used to analyze the cluster validation of the results obtained from the distance measure.
Enabling outdoor MR capabilities for head mounted displays: a case study

This research work covers generic approaches to determine the outdoor position and orientation of an augmented reality device due to the lack of outdoor capability of depth-sensor or environmental recognition-sensor-based devices currently available on the market. The determination of the orientation is primarily achieved with an attitude heading reference system (AHRS) for a rough estimation. Based on a connected/built-in video camera the accuracy at minor changes of the orientation is enhanced by applying registration to assess the differences in orientation between two video frames, compensating for gyroscope drift errors. The position determination is achieved using GPS with a rover and base station real time kinematic beacon system to achieve enhanced precision. Results show that due to sensor application AR hardware considered for indoor use can be retooled to properly work outdoors, at large distances and even inside running vehicles. Thus, future implementation of applications in various domains is facilitated.
On ternary symmetric bent functions

This work was motivated by the fact that in the binary domain there are exactly 4 symmetric bent functions for every even n. A first study in the ternary domain shows very different properties. There are exactly 36 ternary symmetric bent functions of 2 variables, at least 12 ternary symmetric bent functions of 3 variables and at least 36 ternary symmetric bent functions of 4 variables. Furthermore the concept of strong symmetric bent function is introduced. To generate ternary symmetric 2k-place bent functions the tensor sum of two k-place ternary symmetric and the Maiorana Method were analyzed and combined with a set of spectral invariant operations. For n = 3 ternary symmetric bent functions were studied on a class of bent functions in the Reed-Muller domain, and a special adaptation of the tensor sum method was used, obtaining 18 ternary strong symmetric bent functions.
Scalable multi-view stereo using CMA-ES and distance transform-based depth map refinement

Recovering three-dimensional structure from images is a long-standing ill-posed inverse problem in computer vision. This paper presents a simple and highly scalable method to reconstruct dense 3D point cloud from multi-view images by estimating per-pixel depth using an evolutionary computation technique – CMA-ES. The proposed method uses ZNCCbased template matching to reconstruct fine details of textured regions and DAISY-based feature matching to reconstruct smooth surface of homogeneous regions. We handle the problem of reconstructing large homogeneous regions using distance transform-based adaptive median filtering. The proposed method is highly scalable since pixels are processed independently at all stages of reconstruction – depth map estimation, refinement, and fusion. This enables the proposed method to be parallelized at the pixel-level, unlike most existing methods that can only be parallelized at the image-level. Experimental results on Middlebury benchmark dataset demonstrate the robustness and efficacy of the proposed method in reconstructing textured as well as homogeneous regions.
PENGEMBANGAN POP UP BOOK BERBASIS KARAKTER NASIONALISME SEBAGAI UPAYA MENINGKATKAN LITERASI SISWA SD

This study aims to determine the feasibility and effectiveness of pop up books based on nationalism characters to improve literacy of second grade elementary school students. The research model used is the research and development model of Borg &amp; Gall (1983: 775-776). Assessment of teaching material products that have been filled in by media experts, the final criteria from the aspects obtained are based on the results of the conversion of quantitative data into a value on a scale of 5, The number of assessments from validation conducted by media experts is 38, then the results of the assessment from media experts are in the range score X&gt; 33.54 which overall the result is very good. While the results of discussions with class II teachers at SD NU Ma'arif Nurul Jannah Kulon Progo, the final criteria from the aspects obtained are based on the results of the conversion of quantitative data into a value on a scale of 5, the number of assessments made by the teacher is 72, then the results of the teacher's assessment are in the range of scores X&gt; 63 which overall the results are very good. Based on the independent sample t-test, it shows that the t value is equal to -18.256. Meanwhile, the t table value on df is equal to 10 and α of 0.05 (sig.2-tailed) shows that the t table value is ± 2,160. This value is much greater than the calculated t value. So it can be concluded that ho is rejected. This means that the posttest scores of the experimental class and the control class from the operational field test results provide a significant difference. The Pop Up Book developed is said to be effective because there are significant differences between the control class and the experimental class.
A Cloud Computing Task Scheduling Strategy Based on Improved Particle Swarm Optimization

To solve the problem of low efficiency of cloud computing task scheduling, a cloud computing task scheduling strategy based on improved particle swarm optimization algorithm was proposed.Firstly, a particle screening mechanism is established to remove some inferior particles so as to speed up the later convergence rate.Secondly, dynamic parameter adjustment can improve the algorithm search performance and the optimal precision.Then preprocessing of precocity judgment can greatly reduce the algorithm to local optimum.Finally, according to the corresponding characteristics of cloud computing platform, the relevant mathematical model is established, and the improved algorithm is combined with the mathematical model to realize the dynamic scheduling of cloud computing resources.The proposed improved particle swarm optimization algorithm is simulated and compared with other scheduling algorithms to analyze the performance of the improved particle swarm optimization algorithm in scheduling.Experimental analysis shows that the improved particle swarm optimization algorithm proposed in this paper achieves better results.
Multi-Transmitter Physical Layer Authentication Using Channel State Information and Deep Learning

Efficient authentication across wireless devices improves the security of the overall network and can be accomplished at the physical layer. By stratifying privilege levels for different devices, security is further improved. We propose the use of machine learning and received multiple-input multiple-output communications channel information to identify trusted transmitters. Our approach uses received channel state information at various signal to noise levels from multiple transmitters to train a deep neural network to make an authentication decision. We show that an adversarially-trained classifier network can correctly differentiate among transmitters using very few labeled samples from a greater dataset.
Readability Analysis of Laws Related to Public Financial Responsibility and State Budget: A Comparison of Selected Countries

In this study, various acts including regulations of public financial management, fiscal responsibility, and state budget in the selected six countries were subjected to different readability tests, and an international comparison was made. The fiscal responsibility act of six countries – Turkey, the UK, India, Australia, Canada, and Pakistan – were included in the study and analyzed. Each country was analyzed under its official language. Since English is an official language of all of the countries except for Turkey, the authors have evaluated the fiscal responsibility acts of these countries using the following readability tests: Flesch Reading Ease, Flesch-Kincaid, Gunning-Fog, and Dale-Chall. Additionally, Public Financial Management and Control Law No. 5018 approved in Turkey was analyzed by the Ateşman Readability Test which was uniquely designed for Turkish grammar rules. The acts discussed in the study were analyzed not only as a whole but also in parts and subsections. According to the results of the study, the levels of readability of the existing laws in most of the selected countries are very difficult to understand for a university graduate. However, when the readability level of the British Budget Responsibility and National Audit Act tested as parts and subsections and a whole, it was rated at a level a university student could understand. This study analyses the readability and intelligibility of acts related to fiscal responsibility and the state budget in six selected countries, adopting Anglo-Saxon public administration model and making an inter-country evaluation. Since it is important that citizens have enough information about legislation for a citizen-oriented understanding, a legislation system that is understood by the larger part of the society is essential.
Agent-based Control Architecture for Mobile Robot Navigation

The problems of mobile robotics relate to the design and the study of functions of perception decision and action and the coherent integration of these functions in a physical machine.This article proposes to use the paradigm multi-agents and fuzzy logic in the design of the control architecture for the mobile robot navigation in an environment constrained. The control architecture is conceived to tackle various problems produced during navigation. It consists of four agents : agent perception, agent feasibility, agent of locomotion and agent fuzzycontroller. This last uses in its reasoning the fuzzy logic principle. The method used enabled us to have an intelligent system able to adapt to all the changes in the environment of navigation.
Particle based Optimization for Predictive Energy Efficient Data Center Management

Data centers are energy-hungry infrastructures that provide cloud computing services. The growing number of data centers in use has led to a drastic increment of the energy consumption associated to these facilities, causing environmental concerns. For that reason, efficient management strategies are needed in order to reduce the energy consumption while the quality of service is kept. This paper presents a unified management approach for the thermal and workload distribution problem in data centers, shaped as a Model Predictive Control problem. The corresponding optimization problem is intractable for conventional solvers because the model is based on multiple queues and the decision variables are a mix of integer and real valued ones. A highly parallelizable particle based optimization algorithm is proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy.
Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction

Abstract The data of Internet of Vehicles (IoV) can be used to evaluate the driving safety risk of auto insurance policyholder and provide technical means for Usage Based Insurance (UBI). There are many types of IoV data, such as continuous or ordinal, categorical, binary etc., which contain highly sparse and dimensional features after One-Hot processing, thus they learning the interaction between critical features and training predictive model difficult. Furthermore, some of the available data have been desensitized, so it is impossible to perform feature engineering based on experience. We propose an end-to-end deep learning framework named Deep Attentional Factorization Machine (DeepAFM), which combines the power of attentional factorization machine with deep learning for feature learning in a new neural network architecture. Compared with existing deep learning models, our approach can learn the weighted interactions between various features effectively by introducing the structure of feature fields without feature engineering. Experimental results showed that our model yields excellent results in real-world data.
Gated Relational Graph Attention Networks

Relational Graph Neural Networks (GNN) are a class of GNN that are capable of handling multi-relational graphs. Like all GNNs, they suffer from a drop in performance when training deeper networks, which may be caused by vanishing gradients, over-parameterization, and oversmoothing. Previous works have investigated methods that improve the training of deeper GNNs, which include normalization techniques and various types of skip connection within a node. However, learning long-range patterns in multi-relational graphs using GNNs remains an under-explored topic. In this work, we propose a novel GNN architecture based on the Graph Attention Network (GAT) that uses gated skip connections to improve long-range modeling between nodes and uses a more scalable vector-based approach for parameterizing relations. We perform an extensive experimental analysis on synthetic and real data, focusing explicitly on learning long-range patterns. The results indicate that the proposed method significantly outperforms several commonly used relational GNN variants when used in deeper configurations and stays competitive to existing architectures in a shallow setup.
Thermal Characterization of a Tier0 Datacenter Room in Normal and Thermal Emergency Conditions

Datacenters are at the heart of the AI, Industry 4.0 and cloud revolution. A datacenter contains a large number of computing nodes hosted in a large temperature-controlled room. Due to the increasing total power and power density of computing nodes, the overall datacenter compute capacity is often capped by peak power consumption and temperature bottlenecks. To preserve the homogeneous performance assumption between all the nodes, complex cooling solution are required, but they might not be sufficient. In this work, we analysed and characterised the thermal properties of a Tier0 datacenter deploying advanced hybrid cooling technologies: specifically, we studied the spatial and temporal heterogeneity during production and cooling emergency hazards. This paper gives first quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial thermal heterogeneity which could be exploited by thermal-aware job scheduling and datacenter-room run-time workload adaptation and distribution.
BERT Enhanced Neural Machine Translation and Sequence Tagging Model for Chinese Grammatical Error Diagnosis

This paper presents the UNIPUS-Flaubert team’s hybrid system for the NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED). As a challenging NLP task, CGED has attracted increasing attention recently and has not yet fully benefited from the powerful pre-trained BERT-based models. We explore this by experimenting with three types of models. The position-tagging models and correction-tagging models are sequence tagging models fine-tuned on pre-trained BERT-based models, where the former focuses on detecting, positioning and classifying errors, and the latter aims at correcting errors. We also utilize rich representations from BERT-based models by transferring the BERT-fused models to the correction task, and further improve the performance by pre-training on a vast size of unsupervised synthetic data. To the best of our knowledge, we are the first to introduce and transfer the BERT-fused NMT model and sequence tagging model into the Chinese Grammatical Error Correction field. Our work achieved the second highest F1 score at the detecting errors, the best F1 score at correction top1 subtask and the second highest F1 score at correction top3 subtask.