Reviewerly/exHarmony huggingface dataset:
Paradigm of open online access to world knowledge resources: the era of transformation of libraries into the scientific community of the Internet

Paradigm of open online access to world knowledge resources: the era of transformation of libraries into the scientific community of the InternetThe article reflects the effectiveness and public importance of the paradigm of free access to the world electronic resources of knowledge of modern libraries of universities, institutes, research organizations.The library as the oldest cultural Institute serves for the benefit of society, collecting and storing huge volumes of works of press and writing, and taking into account the introduction of information technologies, telecommunication technologies, "end-to-end" -innovative technologies, the prospects of using artificial intelligence systems as one of the tools of scientists, graduate students and students, not only in paper but also in electronic format, which contribute to cultural, educational, scientific and professional activities.The author is convinced that knowledge is such a phenomenon that modern society needs, and the development of society and all its institutions, including the state, is possible only with highly educated citizens who have not only knowledge, but also features of a highly moral person.In the era of digital technologies, the active integration of electronic libraries into the world library system continues, libraries as subjects organize and improve the production process, taking into account the introduction of scientific and technological progress, as one of the global factors determining the huge potential of libraries.Thanks to the international global Internet and access to library resources online, scientists around the world have the opportunity to comprehensive and interactive access to the knowledge accumulated throughout the history of mankind in various scientific fields.Reputable scientists, young scientists and researchers seek to place their work in journals with a high impact factor, the publication of which, unfortunately, is quite expensive.Another part of the scientific community cannot, at no cost, get access to their publications.The author wants to draw attention to the fact that there is a way out of this situation on the conditions of conscientious compliance with international legal obligations in the recognition of the open access paradigm on the principle of legality, and this, in turn, will ensure the continuity of the process of development of science in all its fields in the world.
Naive Regularizers for Low-Resource Neural Machine Translation

Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios.They require large volumes of data and often perform poorly when limited data is available.We show that using naive regularization methods, based on sentence length, punctuation and word frequencies, to penalize translations that are very different from the input sentences, consistently improves the translation quality across multiple low-resource languages.We experiment with 12 language pairs, varying the training data size between 17k to 230k sentence pairs.Our best regularizer achieves an average increase of 1.5 BLEU score and 1.0 TER score across all the language pairs.For example, we achieve a BLEU score of 26.70 on the IWSLT15 English-Vietnamese translation task simply by using relative differences in punctuation as a regularizer.
Study on Mobile Applications for Water Quality in Specific to Irrigation Sector

Since the Water Quality monitoring in the agricultural sector for irrigation activities is complex, this study focuses on investigating the contribution of existing Mobile applications for better monitoring and managing the irrigation water quality. The native platforms so-called 'Apps Stores' acted as a source for this study for revealing the contemporary status of mobile application users worldwide in the irrigation sector. This study narrow-down to a detailed study on the usage of Mobile applications for performing the irrigation activities in the Indian region. It reveals the immediate need for a significant number of mobile applications to enhance the farming activities by effective farmer's participation in India. Lastly, this review proposes the suggestions for new developments in the mobile applications for satisfying the target group with the support from the public and private sectors.
Analysis of Seasonal Aspects of Nebulosity on the Project of Fixed Photovoltaic Installations at the City of Belém, Brazil

By using INMET meteorological data, the Radiasol 2 program and calculations performed to estimate the solar zenith angle, this work aims to estimate the effect of the orientation of fixed photovoltaic panels, taking into account the seasonal aspects of the cloudiness at Belém city, Brazil. At first, the periods of the year of smaller and greater cloudiness are identified. Then, the average angles of solar zenith are estimated for the cloudiest season, for the clearest sky season and for the whole year. Thereafter, the Radiasol 2 program is used to estimate the incident solar energy density on the fixed panels inclined at these estimated zenith angles and during one year. This work intends to estimate and to analyze the impact of the solar panels orientation, considering periods of cloudiness, on the generation of electric energy. The results of solar radiation received at inclined planes show that if the photovoltaic panels are inclined considering the average angle of solar incidence for the least cloudy period of the year, a slight increase is observed in the solar radiation received annually at the panels plane, when compared with the radiation received for the calculated mean zenith angle regarding the cloudiest period.
Low-Rank Approximations of Second-Order Document Representations

Document embeddings, created with methods ranging from simple heuristics to statistical and deep models, are widely applicable.Bagof-vectors models for documents include the mean and quadratic approaches (Torki, 2018).We present evidence that quadratic statistics alone, without the mean information, can offer superior accuracy, fast document comparison, and compact document representations.In matching news articles to their comment threads, low-rank representations of only 3-4 times the size of the mean vector give most accurate matching, and in standard sentence comparison tasks, results are state of the art despite faster computation.Similarity measures are discussed, and the Frobenius product implicit in the proposed method is contrasted to Wasserstein or Bures metric from the transportation theory.We also shortly demonstrate matching of unordered word lists to documents, to measure topicality or sentiment of documents.
An adaptive gravitational search algorithm for global optimization

&lt;span&gt;Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.&lt;/span&gt;
La lingua per lo studio e la formazione degli insegnanti: l’esperienza di un laboratorio di formazione

The present paper proposes some considerations deriving from an educational workshop dedicated to teachers of lower-secondary schools in the province of Forli-Cesena. A brief description of the theoretical framework is followed by a description of how the workshop was structured, which topics were covered, the reasons behind the choice of topics and the reflections that have arisen, in order to draw some concluding remarks on this experience.
Implementing Randomized Salt on Round Key for Corrected Block Tiny Encryption Algorithm (XXTEA)

Enhancement of an algorithm is insignificant unless proven. This study implemented a salting algorithm to the round key for the corrected block tiny encryption algorithm otherwise known as XXTEA, to further enhanced its security against known cryptanalysis against it. The enhanced XXTEA (eXXTEA) showcases a 4.97% improvement on the avalanche effect compared to the original algorithm. Moreover, eXXTEA passed all randomness tests identified while the original algorithm failed on the frequency test. It's worth noting that there's a vast difference between the p-value score between those two.
Orthogonal Graph Regularized Nonnegative Matrix Factorization for Image Clustering

Since high-dimensional data can be represented as vectors or matrices, matrix factorization is a common useful data modeling technique for high-dimensional feature representation, which has been widely applied in feature extraction, image processing and text clustering. Graph regularized nonnegative matrix factorization (GNMF) incorporates the non-negativity constraint and manifold regularization simultaneously to achieve a parts-based meaningful high-dimensional data representation, which can discover the underlying local geometrical structure of the original data space. In order to reduce the redundancy between bases and representations, and enhance the clustering power of NMF, three orthogonal variants of GNMF are proposed, which incorporates the orthogonal constraints into GNMF model. The optimization algorithms are developed to solve the objective functions of Orthogonal GNMF (OGNMF). The extensive experimental results on four real-world face image data sets have confirmed the effectiveness of the proposed OGNMF methods.
DEVELOPMENT OF A MULTI CAMERA CALIBRATION FOR AN ANALYSIS AND MANIPULATION SYSTEM OF BIOLOGICAL SAMPLES IN PETRI DISHES

Abstract. Cameras can provide accurate 3D positioning for a robotic system. Calibration of interior orientation as well as the relative orientation when using multiple cameras is a necessary step. Integration of the automatic calibration process into the robot system is useful to provide a simple system for the user. The paper presents a method for calibrating a multi camera system using the open source Ceres solver and compares it to a commercial software. The motivation is a measurement system for laboratory automation where biological samples in Petri dishes will be manipulated with different tools. For this application we will examine the camera setup, show first results of the calibration and the possible accuracy. Lastly we will draw conclusions for the final system prototype and necessary improvements to be implemented in order to provide accurate measurements.
Graph Drawing with Morphing Partial Edges

A partial edge drawing (PED) of a graph is a variation of a node-link diagram. PED draws a link, which is a partial visual representation of an edge, and reduces visual clutter of the node-link diagram. However, more time is required to read a PED to infer undrawn parts. The authors propose a morphing edge drawing (MED), which is a PED that changes with time. In MED, links morph between partial and complete drawings; thus, a reduced load for estimation of undrawn parts in a PED is expected. Herein, a formalization of MED is shown based on a formalization of PED. Then, requirements for the scheduling of morphing are specified. The requirements inhibit morphing from crossing and shorten the overall time for morphing the edges. Moreover, an algorithm for a scheduling method implemented by the authors is illustrated and the effectiveness of PED from a reading time viewpoint is shown through an experimental evaluation.
A Stochastic Trust-Region Framework for Policy Optimization

In this paper, we study a few challenging theoretical and numerical issues on the well known trust region policy optimization for deep reinforcement learning.The goal is to find a policy that maximizes the total expected reward when the agent acts according to the policy.The trust region subproblem is constructed with a surrogate function coherent to the total expected reward and a general distance constraint around the latest policy.We solve the subproblem using a preconditioned stochastic gradient method with a line search scheme to ensure that each step promotes the model function and stays in the trust region.To overcome the bias caused by sampling to the function estimations under the random settings, we add the empirical standard deviation of the total expected reward to the predicted increase in a ratio in order to update the trust region radius and decide whether the trial point is accepted.Moreover, for a Gaussian policy which is commonly used for continuous action space, the maximization with respect to the mean and covariance is performed separately to control the entropy loss.Our theoretical analysis shows that the deterministic version of the proposed algorithm tends to generate a monotonic improvement of the total expected reward and the global convergence is guaranteed under moderate assumptions.Comparisons with the state-of-the-art methods demonstrate the effectiveness and robustness of our method over robotic controls and game playings from OpenAI Gym.
Image Super Resolution Using Fractal Coding and Residual Network

Fractal coding techniques are an effective tool for describing image textures. Considering the shortcomings of the existing image super-resolution (SR) method, the large-scale factor reconstruction performance is poor and the texture details are incomplete. In this paper, we propose an SR method based on error compensation and fractal coding. First, quadtree coding is performed on the image, and the similarity between the range block and the domain block is established to determine the fractal code. Then, through this similarity relationship, the attractor is reconstructed by super-resolution fractal decoding to obtain an interpolated image. Finally, the fractal error of the fractal code is estimated by the depth residual network, and the estimated version of the error image is added as an error compensation term to the interpolation image to obtain the final reconstructed image. The network structure is jointly trained by a deep network and a shallow network. Residual learning is introduced to greatly improve the convergence speed and reconstruction accuracy of the network. Experiments with other state-of-the-art methods on the benchmark datasets Set5, Set14, B100, and Urban100 show that our algorithm achieves competitive performance quantitatively and qualitatively, with subtle edges and vivid textures. Large-scale factor images can also be reconstructed better.
Agricultural Field Coverage Using Cooperating Unmanned Ground Vehicles

Abstract In this paper, a distributed algorithm with obstacle avoidance capability is presented to deploy a group of ground robots for field-based agriculture applications. To this end, the field (consisting of many plots) is first modeled as a directed graph, and the robots are deployed to collect data from some important areas of the field (e.g., areas with high water stress or biotic stress). The key idea is to formulate the underlying problem as a locational optimization problem and then find the optimal solution based on the Voronoi partitioning of the associated graph. The proposed partitioning method is validated through simulation studies, as well as experiments using a group of mobile robots.
When Teacher Education Goes Mobile: A Study on Complex Emergence

ABSTRACT The popularity of mobile devices brings on new challenges for teacher education. Based on complexity theory, this qualitative study1 seeks to better understand the dynamics of a social group, especially the conditions for the emergence of learning in a mobile educational experience. Sixty in-service teachers in Brazil participated in a six-week course via WhatsApp. The analysis of the answers to questionnaires and the corpus generated by the teachers’ interactions indicate that the devices’ mobility favored interactions, control distribution, and social repertoire of possibilities. The findings also indicate that the teachers recognize the potential of mobile technology for mediating learning experiences.
Crowd Counting via Residual Multi-Scale Convolutional Neural Network

Scale-adaptive Convolutional Neural Network (SaCNN) has been proposed for crowd counting that is a practical application of machine learning in video surveillance. However, SaCNN suffers from an issue of degradation caused by its deep structure. Therefore, this paper proposes a Residual Multiscale Convolutional Neural Network (RMsCNN) to address the degradation issue. Based on the backbone of SaCNN, RMsCNN adopts residual learning and maps the image to its crowd density map for crowd counting. Crowd counting can be implemented by integrating the estimated density map. Experiments are conducted on Shanghaitech and UCF CC 50 datasets. Experimental results indicate that RMsCNN has a better performance compared with the state-of-the-art models.
Development of system of education in soviet vocational educational institutions in 1950s – 1990s

The purpose of this historical and pedagogical research is to consider the problem of periodization of the development of the system of educating students in Soviet vocational schools in the 1950s – 1990s. Particular attention is paid to the definition of the chronological framework of the stages of development of the Soviet system of education in vocational education. On the basis of the conducted research the structure of periodization and the key events influencing qualitative changes in the content of the Soviet system of education of future workers are defined.
Attack Trees: A Notion of Missing Attacks

Attack trees are widely used for security modeling and risk analysis. Classically, an attack tree combines possible actions of the attacker into attacks. In most existing approaches, an attack tree represents generic ways of attacking a system, but without taking any specific system or its configuration into account. This means that such a generic attack tree may contain attacks that are not applicable to the analyzed system, and also that a given system could enable some attacks that the attack tree did not capture. To overcome this problem, we extend the attack tree setting with a model of the analyzed system, allowing us to introduce precise path semantics of an attack tree and to define missing attacks. We investigate the missing attack existence problem and show how to solve it by calls to the NP oracle that answers the trace attack tree membership problem; the latter problem has been implemented and is available as an open source prototype.
IMPROVING STUDENTS’ WRITING ABILITY ON DESCRIPTIVE TEXT BY USING INTENSIVE READING

AbstrakThis research is conducted in order to improve students’ writing ability by using intensive reading at tenth grade of SMAN 14 Kabupaten Tangerang. In conducting this research the writer used qualitative descriptive as the method of the research. In this research used purposive sampling with the sample was 36 participants. To collect and analyzed the data, the writer used the information from observation, students’ writing test and interview. The result of observation and interview in this research showed that intensive reading can improve students’ writing ability on descriptive text. The result also supported by students’ writing test. It can be seen from students’ pre-test and post-test. The average score of pre-test was 30.1 or 35% and the average score of post-test was 55.4 or 65% of 36 students. The average score of post-test showed higher with increase score 25.3 or 30% of 36 students than their pre-test. It means intensive reading can improve students’ writing ability especially on descriptive text. From the result, the writer concluded that teaching writing descriptive text using intensive reading at tenth grade of SMAN 14 Kabupaten Tangerang can improve their writing ability. For the teachers and the further researchers, it is recommended that to implement this method in English classroom to improve students’ writing skill.Kata Kunci: descriptive text, intensive reading, writing ability
IMPLEMENTASI DATA MINING DENGAN METODE REGRESI LINEAR BERGANDA UNTUK MEMPREDIKSI DATA PERSEDIAAN BUKU PADA PT. YUDHISTIRA GHALIA INDONESIA AREA SUMATERA UTARA

PT. Yudhistira Ghalia North Sumatra. Where Inventory (stock) of goods is an important thing in a company for data collection or checking activities in order to find out the amount of goods that are used up and goods that will be needed in a company. Inventory of goods is always needed in company activities. So that in the supply of books has been delayed for making stock and excess stock making of books. In this study, multiple linear regression method will be used to predict book inventory data. So for that we need to predict book inventory data for the future how many books should be in stock. Multiple linear regression algorithm has advantages such as generalizing and extracting from certain data patterns, being able to acquire knowledge even though there is no certainty, and being able to do calculations in parallel so that the process is shorter. After being predicted, it will be possible to produce results that can be used in the future so that it can help PT. Yudhistira especially the part of the inventory of goods which must be provided in the following month.Keywords: Data Mining, Inventory, Multiple Linear Regression Algorithms
Analysis of Workflow and Structuring of the Problem Element for Workflow Database Construction of Radiological Technologists in Radiation Therapy

The work of radiological technologists is changing and more complicated because of the development of medical technology and implementation of information technology (IT). Although the cases of incident and accident have been reported, they have not been comprehensively analyzed in the workflow for radiotherapy. In this study, we visualized the workflow of radiological technologists in radiotherapy and revealed the causes of incidents and accidents. The work process was visualized by drawing workflow map. The structuring of problem was performed with interpretive structural modeling (ISM) method based on graph theory by analyzing of work categorized by safety management. Our results may be able to clarify the work of radiological technologists leads to the reduction of incidents and accidents in radiation therapy.
ADVANTAGES AND LIMITATIONS OF LINEAR CANONICAL CORRELATION ANALYSIS (LCCA)

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 6332-6338ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1527Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Correction to: On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems

The article was originally published by the journal with an incorrect title.