Reviewerly/exHarmony huggingface dataset:
Identification of Wind Turbine using Fractional Order Dynamic Neural Network and Optimization Algorithm

In this paper, an efficient technique is presented to identify a 2500 KW wind turbine operating in Kahak wind farm, Qazvin province, Iran. This complicated system dealing with wind behavior is identified by using a proposed fractional order dynamic neural network (FODNN) optimized with evolutionary computation. In the proposed method, some parameters of FODNN are unknown during the process of identiﬁcation, so a particle swarm optimization (PSO) algorithm is employed to determine the optimal values by which a fractional order nonlinear system can be completely identified with a high degree of accuracy. These parameters are very effective to achieve high performance of FODNN identifier and they include fractional order, initial values of states and weights of FODNN, and numerical algorithm step size for solving FODNN equation. Simulation results confirm the efficiency of the proposed scheme in term of accuracy. Furthermore, comparison of the results achieved by the proposed method and those of the integer order dynamic neural network (IODNN) depicts higher accuracy of the proposed FODNN.
Context Analysis and Estimation of Mobile Users by using Bio-signals and Sensor Data

The sensor data obtained from mobile and wearable devices are useful to analyze and estimate user's context, but also user's bio-signals are, because they may reflect user's psychological aspects in the corresponding context. Therefore, in this paper, we focus on context analysis and estimation of mobile users by using bio-signals and sensor data of mobile devices. For the analysis and estimation, various machine learning methods are applied to classify the data into pre-defined six contexts. The evaluation shows that Gradient Boosting Decision Tree achieves the highest classification accuracy of about 80% in supervised methods, and Sparse Representation-based Classification achieves more than 90% accuracy. The results suggest that the context analysis and estimation can be done accurately by using bio-signals and sensor data.
USING COOPERATIVE LANGUAGE LEARNING IN IMPROVING THE STUDENTS’ PRONUNCIATION ACCURACY AND FLUENCY

This research aimed to find the improvement of the students’ Pronunciation Accuracy and Fluency by using Cooperative Language Learning at Akademi Bahasa Asing Balikpapan at Third Semester Students in Balikpapan East Kalimantan in the 2018/2019 Academic Year. Cooperative Language Learning was used in a Classroom Action Research (CAR). The research had been conducted in two cycles, each cycle consist of four meetings. It employed Pronunciation test as instrument. A number of research subjects were 37 students in third semester. The instruments were test and observation. The research findings showed that Cooperative Language Learning could improve the students’ Pronunciation mastery from cycle I to cycle II. The students’ achievement from D-Test to cycle I was 12.50%, and after finishing cycle II the students’ achievement became 16.27%. Based on the research findings, the researcher concludes that Cooperative Language Learning gave a significant contribution in learning process of Pronunciation accuracy and fluency
Fuzzy System for Obstacle Detection and Avoidance for Mobile Robot

We propose a system for detection and avoidance of obstacles based on the use of fuzzy logic. Basically, data coming from a ZED stereo camera are used for the decision making based on fuzzy predicates. Validation and testing of the algorithms are provided under the ROS (Robot Operating System), which runs together with Gazebo 7. The developed tool is an important step towards solving problems related to the techniques and methods or algorithms that can be used in mobile robots for detection and avoidance of obstacles and other objects on their way. The proposed system proved to be very robust in the tests carried out in the simulated environment.
Query Performance Evaluation of Sensor Data Integration Methods for Knowledge Graphs

In this paper, a Smart Data Service, based on Semantic Web technology is introduced, which supports the control engineer during the data-driven model development process by enabling enhanced data analysis. As a perquisite for such a service, sensor data consisting of semantic meta data as well as time series data have to be integrated into a so-called knowledge graph. Therefore, three different integration approaches, found in the literature, were evaluated and compared regarding their query execution performance. The characteristics and limitations of these three methods are discussed to specify the conditions for their specific utilization.
Pengaruh Parameter Milliampere-Second (mAs) terhadap Kualitas Citra Dan Dosis Radiasi Pada Pemeriksaan CT scan Kepala Pediatrik

Radiographers are responsible for administering radiation doses according to the principle of As Low As Reasonably Achievable (ALARA), to optimize the use of CT parameters to produce good image quality by minimizing radiation doses. The purpose of this study was to determine the effect of mAs parameters on image quality and radiation dose received by pediatric CT head examination patients. The research method to perform image processing results of examination using radiant viewer and analysis of value Contrast to noise ratio (CNR) as a parameter of image quality and CTDIvol to determine the estimated radiation dose, then an analysis is performed to determine whether there is an influence of mAs on the CNR and CTDIvol values. The mean value of kV 116 ± 8.28, the mean value of parameter mAs 161.5 ± 63.46, the mean SNR parameter value is 7.5 ± 0.8, the mean return value of CNR parameter was 0.318 ± 0.291, the mean value of the CTDIvol parameter is 18.68 mGy ± 4.55 mGy, the average value of DLP parameters was 408.7 mGy x cm ± 105.2 mGy x cm, the mean value of parameter DLP is 441.2 mGy x cm ± 155.2 mGy x cm. With linear regression analysis of test results between mAs to CNR is R2 = 0.045. then to the results of linear regression between mAs against CTDI is R2 = 0704, the estimated radiation dose limit for the average value of CTDIvol 18.68 mGy and the average value of DLP 408.7 mGy x cm, this means the protocol CT scan of the head of the pediatric use is still within safe limits by Diagnostic Reference Level of BAPETEN in 2018 to pediatric head CT examination
The Need for Open Science

Open Science is about sharing knowledge where research data and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods.&nbsp; The availability of these data publicly should promote the translation of research output into innovation and new technologies.&nbsp; In addition, making research data and knowledge publicly available to others would help to build trust on the research findings and could support public policies and further investments.
How Do People Make Relevance Judgment of Scientific Data?

Many efforts have been made to explore user relevance judgment for documents, images, web pages and music in the field of information retrial. However, there is a lack of attention to scientific data even when scientists and researchers are facing an increasing data deluge. In this study, we carried out a two-phase (first exploratory and then empirical) research to explore relevance judgment patterns of scientific data users. In the exploratory study, we interviewed 23 subjects who participated in a national competition related to scientific data. Five relevance criteria (RC) and seven paths of their usage were identified by content analysis of the transcribed records of the interview. Based on the results of the first phase, seven hypotheses were proposed and verified by partial least squares structural equation modelling (PLS-SEM). The study identified five RC, i.e. topicality, accessibility, authority, quality and usefulness used by scientific data users. Three patterns were identified including 1) data topicality judgment as the first step or starting point, 2) data reliability judgment as the necessary process and 3) data utility judgment as final purpose. These findings provide new understanding of relevance judgement and behaviours of scientific data users, and could benefit the design for cognitive retrieval systems and algorithms specific to scientific data.
Academic mobility as a way to increase intercultural communicative competence and students’ personal social capital

Права:
A Technique for Panorama-Creation using Multiple Images

Image stitching, which is a process of integration of multiple images to create a panoramic image using all contents fitted into one frame, finds wide-spread applications in medical, high resolution digital map, satellite and video imaging.This paper proposes a framework to develop panorama image with multiple images.The framework is an automatic process that takes multiple images, checks correlation of the sequential images and removes overlapping area if exists and creates the panorama.We have done experimentations using different imagesets consisting multiple images with and without overlapping and got satisfactory results.
Economic and Mathematical Modeling of Tracking Paths Process Support of the Region Labor Resources in the Context of Incomplete Information

Abstract The most important when planning the development of individual regions is the question of assessing intermediate and final states. Since the development of the region is planned for a long period of time, the game-theoretic approach is possible to construct its mathematical model. A mathematical model for the development of a particular region is developed, taking into account labor movements as an investment between industries. The mathematical problem is reduced to finding the “optimal” trajectories of the development of the vector criterion of the Pareto-optimal solution. The task is to find the Pareto optimal trajectories with known vector-valued function, Nash arbitration scheme is proposed to choose as the only solution. But the Pareto-optimal set is still not enough to be able to work out a collective solution. It is necessary to determine the “fair”, from the point of view of all the players, method of choosing a terminal point from the Pareto-optimal set, since only one development outcome can really be realized. For this, in the classical theory there are various arbitration schemes.
Automated Regression Testing and Data Analytics using Python

Testing is of utmost importance in industrial domain. Product testing makes sure that, software performs the dedicated task error free. Product testing is the key prerequisite before releasing the product. In order to satisfy the customer's requirements, it is essential to experiment the device against different test conditions. The key idea behind performing regression testing is to check for any recent script changes have not adversely affected the existing functionalities of the device. Data analytics using python is performed to draw necessary conclusions based on the type of test conducted. The aim of this paper is to perform automated regression testing and reusing the existing scripts with new modifications. Data analytics is being carried out on error data to visualize accuracy, deviations and other parameters.
Game theory for privacy-preserving sharing of genomic data

The coalescence of efficient genome sequencing technologies, low-cost computing, and academic and commercial incentives has led to the establishment of genomic data repositories on millions of individuals across the world. Making data available from such repositories may enable important discoveries, but high-profile demonstrations have illustrated that the privacy of individuals could be infringed upon if appropriate safeguards are not set in place. A wide array of deterrents have been designed to protect genomic data privacy, some of which are technical (e.g., obfuscating data), while others are more social (e.g., legal contracts). However, it has not been obvious which protections need to be adopted. Game-theoretic approaches, which model rational interactions among the parties involved, have the ability to optimally balance utility and risks in genomic data-sharing scenarios. In this chapter, we review recent advancements in genomic data privacy that rely on game-theoretic approaches, while characterizing their core contributions, as well as their limitations.
Image-quality Improvement of Omnidirectional Free-viewpoint Images by Generative Adversarial Networks

This paper proposes a method to improve the quality of omnidirectional free-viewpoint images using generative adversarial networks (GAN). By estimating the 3D information of the capturing space while integrating the omnidirectional images taken from multiple viewpoints, it is possible to generate an arbitrary omnidirectional appearance. However, the image quality of free-viewpoint images deteriorates due to artifacts caused by 3D estimation errors and occlusion. We solve this problem by using GAN and, moreover, by focusing on projective geometry during training, we further improve image quality by converting the omnidirectional image into perspective-projection images. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Retinex based Reflectance Decomposition Using Convolutional Sparse Coding

Color constancy is a feature of the human visual system, which detects the relative inconsistency of the perceived color of an object so that the perceived color may not be changed under various illumination conditions, even when the conditions for observing color are changed. Thus, the Retinex theory was designed in consideration of this color constancy. The physics based Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of the object. However, if there are many detail areas in the image or the illumination changes rapidly, the illumination and reflectance may not be decomposed properly, because of the violation of the smoothness constraint on illumination. In this paper, we use the convolutional sparse coding model to represent the reflectance in more detail. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in experimental results. Consequently, we can decompose Retinex based illumination and reflectance more precisely, then, reduce the perception gap between humans and machines.
Self-supervised generative adversarial network for electronic cleansing in dual-energy CT colonography

CT colonography (CTC) uses abdominal CT scans to examine the colon for cancers and polyps. To visualize the complete region of colon without possibly obstructing residual materials inside the colon, an orally administered contrast agent is used to opacify the residual fecal materials on CT images followed by virtual cleansing of the opacified materials from the images. However, current EC methods can introduce large numbers of residual image artifacts that complicate the interpretation of the virtually cleansed CTC images. Such artifacts can be resolved by use of dual-energy CTC (DE-CTC) that provides more information about the observed materials than does conventional single-energy CTC (SE-CTC). We generalized a 3D generative adversarial network (3D-GAN) model into a self-supervised electronic cleansing (EC) scheme for dual-energy CT colonography (DE-CTC). The 3D-GAN is used to transform the acquired DE-CTC volumes into a representative cleansed CTC volume by use of an iterative self-supervised method that adapts the scheme to the unique conditions of each case. Our preliminary evaluation with an anthropomorphic phantom indicated that the use of the 3DGAN EC scheme with DE-CTC features and the self-supervised scheme generates EC images of higher quality than those obtained by use of SE-CTC or conventional training samples only.
A New Project for Provenance Research in Brazil

Provenance research in Latin America generally, and Brazil in particular, lags well behind that of European countries. Brazil has a history of library dispersals over the past 300 years: the Jesuits, Benedictines, Franciscans, and Dominicans all built impressive libraries, now largely scattered, with books and manuscripts brought from the old world; cities such as Rio de Janeiro, Salvador, Recife, and Maranhao were cosmopolitan entrepots for wealthy foreigners who left their books there. Since March 2019, the Department of Librarianship of the Universidade Federal do Estado do Rio de Janeiro (Federal University of the State of Rio de Janeiro) has been conducting the research project A Eloquencia dos Livros: Marcas de Proveniencia (‘The Eloquence of Books: Provenance Marks’), co-ordinated by Professor Fabiano Cataldo de Azevedo and Professor Stefanie Cavalcanti Freire. This project is hosted by what was the first library school in Latin America, founded in 1911 at the National Library...
Characterization of color normalization methods in digital pathology whole slide imaging

The color rendering of whole-slide images (WSIs) depends on factors involving the sample, such as tissue type, preparation methods, staining type and staining protocol, as well as equipment, such as the WSI scanner, WSI viewer, and WSI display. Variations in any of these steps may change the color rendering and therefore affect the performance of pathologists in the interpretation of WSIs and the robustness of artificial intelligence algorithms. In the literature, color normalization techniques have been proposed to reduce the color variations. The purpose of this work is to develop an objective approach to characterizing color normalization methods used in digital pathology. We employed color normalization methods to normalize the color rendered by a WSI scanner and then compared the normalized color with the actual scan by that scanner. The normalization errors were evaluated on the pixel level using the CIE color difference &Delta;E metric that have been shown to correlate with visually perceived differences in human vision. A selected set of 310 patch images of breast tissues scanned by two scanners from the ICPR 2014 MITOS &amp; ATYPIA contest was used. Images from one scanner were color normalized to match the color rendering of the other scanner. Four color normalization methods were compared – Macenko, Reinhard, Vahadane, and StainGAN. Experimental results show that average color differences between two scanners in terms of &Delta;E were reduced from 16.2 before normalization to the range of [13.7,16.9] after normalization for the Macenko, Reinhard, Vahadane methods, and to 8.3 for the StainGAN method. Apparently the StainGAN method is significantly superior to the other three methods in terms of the &Delta;E metric. As such, we demonstrated a quantitative method for objectively evaluating color normalization techniques. Future work is needed to explore the relationship of the color fidelity measure and the impact of color normalization on pathologist and AI performance in clinical tasks.
Learning efficient channels with a dual loss autoencoder

In medical imaging systems, task-based metrics have been advocated as a means of evaluating image quality. Mathematical observers are one method of computing such metrics. Although the Bayesian Ideal Observer (IO) is optimal by definition, it is frequently intractable and non-linear. Linear approximations to the IO are sometimes employed to obtain task-based statistics when computing the IO is infeasible. The optimal linear observer for maximizing the SNR of the test statistic is the Hotelling Observer (HO). However, the computational cost for computing the HO increases with image size and becomes intractable for larger images. Channelized methods of reducing the dimensionality of the data before computing the HO have become popular, with efficient channels capable of approximating the HO’s performance at significantly reduced computational cost. State-of-the-art channels have been learned by using an autoencoder (AE) to encode data by employing a known signal template as the desired reconstruction, but the method is dependant on a high-quality estimate of the signal. An alternative to channels is approximating the test statistic directly using a feed-forward neural network (FFNN). However, this approach can overfit when the amount of training data is limited. In this work, a generalized method for learning channels utilizing an AE with dual losses (AEDL) is proposed. The AEDL framework jointly minimizes both task-specific and reconstruction losses to learn a set of efficient channels, even when the number of training images is relatively small. Preliminary results indicate that the proposed network outperforms state-of-the-art methods on the selected imaging task. Additionally, the AEDL framework suffers from less overfitting than the FFNN.
Reversible Watermarking scheme for Authentication and integrity control in Biometric Images

In this era of digital world, authentication and integrity of biometric data is really imperative for data communication over erroneous network as well as for storage and processing in large database. In order to attain aforesaid, a watermark comprising details of ownership can be embedded in the biometric image. Further, it is required that the modified data can be reversed back to original data without any information loss, after the details are retrieved. This paper proposes a completely reversible watermarking system preventing any loss of information and solving the issues associated with biometric authentication and integrity. The proposed technique utilizes Discrete Cosine Transform (DCT) for compact features extraction. Based upon the features extracted from the original image and watermark, a secure key is generated. This key is required to retrieve the watermark from the modified image. Experimental outcomes reveal that proposed methodology gives high values of Peak Signal to Noise Ratio and Normalized Correlation which are comparable with the state-of-art work.
Features of the arrangement of pedagogical support for students during an online course as part of extracurricular independent work

О. В. Тихонова, О. А. Чихачева, Н
MOBILE-ASSISTED LANGUAGE LEARNING IN A UNIVERSITY CONTEXT IN VIETNAM: STUDENTS’ ATTITUDES

The study concerns the attitudes towards Mobile-assisted language learning (MALL) of the participants in a university. Using the mixed-method research design with survey questionnaire as the main data collection tool, the research gained some significant findings and successfully met the aims of the study. The findings from both qualitative and quantitative data reveal that the participants basically held positive attitudes towards the use of MALL inside classroom and towards MALL in general. It is suggested that MALL be adopted as a new approach in English language teaching and learning. Moreover, some measures should be taken to develop the digital literacy of both instructors and learners.