Reviewerly/exHarmony huggingface dataset:
Evaluation of Depth Cameras for Use as an Augmented Reality Emergency Ruler.

Children are rarely affected by medical emergencies. The experience of doctors or paramedics with child emergencies is correspondingly poor. The anatomical features and individual calculations make such an emergency much more error-prone than a comparable adult emergency. Particularly in dose calculations, critical errors occur time and again. Since these calculations are based on the child's weight, which is preclinically often derived from the size of the child, the number of errors can be minimized with an assistance service that performs all calculations based on the size. Technically, it is possible to detect the size with a depth camera, which is occasionally installed in smartphones or head-mounted displays. In order to investigate to what extent these cameras provide precise results, a study with 33 children was carried out. The children were measured with both an emergency ruler and an augmented reality app with associated smartphone with depth camera. The result is that the depth camera does not provide significantly different results than an emergency ruler. This allows further research, e.g. the automatic recognition of patients with the help of machine learning or usability studies, to be tackled.
Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions

Global horizontal irradiance (i.e., shortwave downward solar radiation received by a horizontal surface on the ground) is an important geophysical variable for climate and energy research. Since solar radiation is attenuated by clouds, its variability is intimately associated with the variability of cloud properties. The spatial distribution of clouds and the daily, monthly, seasonal, and annual solar energy potential (i.e., the solar energy available to be converted into electricity) derived from satellite estimates of global horizontal irradiance are explored over the state of Texas, USA and surrounding regions, including northern Mexico and the western Gulf of Mexico. The maximum (minimum) monthly solar energy potential in the study area is 151–247 kWhm−2 (43–145 kWhm−2) in July (December). The maximum (minimum) seasonal solar energy potential is 457–706 kWhm−2 (167–481 kWhm−2) in summer (winter). The available annual solar energy in 2015 was 1295–2324 kWhm−2. The solar energy potential is significantly higher over the Gulf of Mexico than over land despite the ocean waters having typically more cloudy skies. Cirrus is the dominant cloud type over the Gulf which attenuates less solar irradiance compared to other cloud types. As expected from our previous work, there is good agreement between satellite and ground estimates of solar energy potential in San Antonio, Texas, and we assume this agreement applies to the surrounding larger region discussed in this paper. The study underscores the relevance of geostationary satellites for cloud/solar energy mapping and provides useful estimates on solar energy in Texas and surrounding regions that could potentially be harnessed and incorporated into the electrical grid.
Comparative Examination of Commonly Used Some Fixatives with Routine Histochemical Staining’s for The Optimal Histological Appearance in The Gill Tissue of Zebrafish

Histopathological studies related to Zebrafish which are used as a model organism in researching the pathogenesis of many diseases increase day by day. Because of the increasing importance of zebrafish research, experiments on this animal model are more prominent and gill tissue is frequently examined in various disease models using zebrafish. As it is known, at the end of the experimental animal model studies, if the histopathological examination is to be done, the tissues should be fixed. The purpose of fixation is to keep cellular and extracellular components in vivo as much as possible. Therefore, the choice of fixation methods and fixatives has a significant effect on tissue processing. In this study, we aimed to optimize fixation techniques and staining protocols for producing ideal slides and histological images of gill tissue in zebrafish. In our study, to determine the optimal histology in the zebrafish-gill tissue, the tissues were fixed with Bouin’s, Carnoy’s, 10% neutral buffered formalin (NBF), Davidson’s, and Dietrich’s solutions. Following the routine tissue processing, the sections were stained with Hematoxylin and Eosin (H&E) and Masson’s Trichrome (MT) stains. Consequently, tissue morphology was best preserved with Bouin’s and NBF solutions. The best results in H&E stain were obtained from tissues fixed with NBF solution, while Dietrich's solution fixation was found to be ideal for MT staining.
The Gopala-Hemachandra universal code determined by straight lines

Variation on the Fibonacci universal code, known as Gopala-Hemachandra (or GH) code, is mainly used in data compression and cryptography as it is a self-synchronizing code.In 2010, Basu and Prasad showed that Gopala-Hemachandra code GH a (n) exists for -20 a -2 and 1 n 100 as well as there are m consecutive non-existing Gopala-Hemachandra codewords in GH -(4+m) (n) column where 1 m 16.In this paper, we have introduced GH code straight line in two-dimensional space where each integral point (a, n) on the GH code straight line represents a unique GH codeword.GH code straight lines confirm the existence of GH codewords for any integer n 1 and integer a -2.Moreover, for a given parameter (a, n), we have introduced two methods to check whether GH codeword exists or not.
Classical and quantum correlations for a family of two-qutrit states

The classical and quantum correlations for a family of two-qutrit states are investigated. For this purpose, a family of two-qutrit states, as well as a set of measurements are proposed. An analytical quantum discord treatment is presented for this family of two-qutrit states. Finally, we compute and illustrate the entanglement of formation, classical mutual information and quantum discord for this family of two-qutrit quantum states.
FUTURE OF CRYPTOCURRENCIES AND BLOCKCHAIN TECHNOLOGY IN FINANCIAL MARKETS

In this paper we are presenting scopeand restrictions on the use of cryptocurrencies in internationalbusiness, banking and in financial marketsThe subject of this research is to analyzeutilization of cryptocurrencies in the internationalbusiness. The goals of the research are to provide tothe researchers and to practitioners and to the scientificand professional public, an overview of currentresearch on blockchain technology in the economyand to determine the impact of the wider use of cryptocurrenciesin international business and their impacton the future of financial markets..The researchwas realized by the method description, literatureanalysis and conducted research. The selected examplesare presenting the possibility of earning, buyingand storing cryptocurrencies, paying with cryptomoneyand invest in them. In the paper we aresearching for an answer to the question “What arethe advantages and disadvantages of using cryptocurrenciesin international payment and what is the securityof using cryptocurrencies in the future?” In theanswer, this paper will present in what direction theuse of crypts will develop in the future. Results arepresenting the blockchain technology, though backgroundtechnology, which would not be known thatcryptocurrencies did not gain popularity, has a brightperspective. Conclusion presents that as long as thetransaction costs are lower than the cost of paymenttransactions, the rational behavior of legal and naturalpersons requires that they should encourage the useof cryptocurrencies among themselves in order to reducethe costs of the transactions when paying and toovercome the existence of an intermediary.
Fast Interval Joins for Temporal SPARQL Queries

Knowledge graphs enriched with temporal information are becoming more and more common. As an example, the Wikidata KG contains millions of temporal facts associated with validity intervals (i.e., start and end time) covering a variety of domains. While these facts are interesting, computing temporal relations between their intervals allows to discover temporal relations holding between facts (e.g., “football players that get divorced after moving from a team to another”). In this paper we study the problem of computing different kinds of interval joins in temporal KGs. In principle, interval joins can be computed by resorting to query languages like SPARQL. However, this language is not optimized for such a task, which makes it hard to answer real-world queries. For instance, the query “find players that were married while being member of a team” times out on Wikidata. We present efficient algorithms to compute interval joins for the main Allen’s relations (e.g., before, after, during, meets). We also address the problem of interval coalescing, which is used for merging contiguous or overlapping intervals of temporal facts, and propose an efficient algorithm. We integrate our interval joins and coalescing algorithms into a light SPARQL extension called iSPARQL. We evaluated the performance of our algorithms on real-world temporal kgs.
Optimization Of Image Processing Techniques In Developing Of Smart Parking System

A parking system is currently a necessity and a common facility found in the campus area of buildings and shops, the management and supervision of this parking lot generally uses third party assistance both in terms of technology and facilities used. At first the parking lot system was still conventional based by using guards in front of the gate and manually recording the entry and exit of the vehicle, a more sophisticated technology was using a camera that was combined with ticketing assistance, officers would check with photos when the car entered and left and strengthened with the use parking tickets, the two technologies have been implemented in many places, the weakness of the system is that there is a need for more than one operator to record and criminal acts will be difficult to detect if a type of vehicle of the same color is stolen because the data from the photos are indistinguishable, To solve this problem, a parking system was created with the help of image processing techniques by recording different types of vehicles even though they were of the same type and color, this system worked using computer vision algorithm rocks with a combination of OCR a Algorithm and surf algorithm, these two algorithms will help record vehicle number-plates while the surf algorithm will record the unique characteristics of the vehicle object in detail so that it will not be change
Metacognitive Feelings, Conflict Detection and Illusion of Linearity

Studies on conflict detection have suggested that people are sensitive to conflict between their heuristic judgment and logical or probabilistic principles, but due to the inhibition failure, they do not disregard appealing heuristic answer. However, these studies were mostly conducted on syllogistic reasoning and base-rate problems. The question is whether findings about conflict detection can be applied to other materials and areas of reasoning. Current study focused on the illusion of linearity, in which people over-rely on linearity heuristic. Therefore, the aim of this study was to examine whether students detect the conflict between heuristic answer and logical/mathematical principles. Participants were 113 secondary school students from Zagreb, Croatia. Data were collected using a computer program, which consisted of instructions, 20 problems (10 linear and 10 non-linear) and sociodemographic questions. Problems were presented randomly in multiple-choice format and had three offered answers (correct answer, distractor, "none of the answers"). Response time for each problem was also measured. Results demonstrated that students mostly solved non-linear problems incorrectly and in accordance with linearity heuristic. Furthermore, the analysis of metacognitive feelings of confidence and difficulty revealed that students detected conflict between heuristic answer and mathematical principles. Moreover, overriding heuristic answer and generating correct answer to non-linear problems resulted in increased response time. Comparison between metacognitive feelings and response time in linear and non-linear problems indicates the importance of processing fluency and inhibition failure in the occurrence of the illusion of linearity.
TOURMALINE AS AN INDICATOR OF TIN OCCURRENCES ОF CASSITERITE-QUARTZ AND CASSITERITE-SILICATE FORMATIONS (A CASE STUDY OF THE VERKHNEURMIYSKY ORE CLUSTER, FAR EAST)

The research focused on the composition of tourmaline from tin ore deposits and ore occurrences within the Verkhneurmiysky ore cluster in the Amur region. The aim of the study is to determine the indicative signs of tourmaline from cassiterite-quartz and cassiterite-silicate formations. This research is based on the materials of a long-term study of the mineralogy of the Far East deposits, conducted at the Mining University under the scientific supervision of Professor Yu.B.Marin. The relevance of the study involves predicting of tin and associated mineralization. For the first time, SIMS and Mössbauer spectroscopy were used to study tourmaline from this region. We identified the typomorphic characteristics of the tourmaline composition, which are proposed to be used as indicators of tin-ore deposits. Typomorphic characteristics of tourmaline from cassiterite-quartz formation: schorl (Mg/(Mg + Fe) = 0.06) with a high content of Al and K; Fe3+/(Fe3+ + Fe2+) = 0.03; ZFe3+ = 1 %; impurities: Nb, LREE (La, Ce, Pr), Be, Bi, F, Li, and Mn; LREE content &gt; 9 ppm; positive Gd anomaly. Typomorphic characteristics of tourmaline from cassiterite-silicate formation: schorl-dravite (Mg/(Mg + Fe) = 0.22) with a high Ca content; Fe3+ / (Fe3+ + Fe2+) = 0.17; ZFe3+ = 9 %; impurities: Zr, Y, Cr, V, Sn, In, Pb, W, Mo, Ti, HREE, Eu, Sr, Sb, and Sc; the content of Y is &gt; 2 ppm, of HREE is &gt; 3 ppm, Eu is &gt; 0.1 ppm. The formation conditions of the cassiterite-silicate ore mineralization were more oxidizing than those of the cassiterite-quartz one. Tourmaline, formed under oxidizing conditions, contains such impurities as Sn, In, Nb, Bi, Sc, and LREE. The content of Sn isomorphic impurity in tourmaline reaches 8000 ppm.
MOBILE LEARNING AND THE FORMATION OF DIGITAL LITERACY IN A KNOWLEDGE SOCIETY

The present article aims to show the emergence of digital technologies in university education as a way for the formation of digital literacy in the contemporary knowledge society.The article analyzes the changes, which occur in university education creating prerequisites for mobile learning.Special attention paid to distance learning, where teachers and learners use digital communication technologies and interactive learning tools.Methodology is based on qualitative methods, under the national project "Digital Media Literacy in the context of "Knowledge Society": state and challenges", № КП-06-Н25/4, 2018, funded by National Science Fund -Bulgaria.The respondents -lecturers and students assessed digital skills and environment in university education.The main conclusions are that more efforts needed to improve the approaches to mobile learning, to create Internet security and to take into account the interests of learners.
Joint Crypto-Compression Based on Selective Encryption for WMSNs

Wireless Multimedia Sensor Networks (WMSNs) have been widely used in many aspects of life such as monitoring aims, risk environments and medical services. However, WMSNs have many challenges due to resources limitation (e.g., restriction in processing, energy and memory). Moreover, security issues related to WMSNs attract many research efforts. Due to limitations of WMSNs resources and their internal encoding, standard methods of data encryption are inappropriate to be used in order to secure data of WMSNs. In this paper, a compression algorithm named Set Partitioning In Hierarchical Tree (SPIHT), jointly with selective encryption during the compression process cycle, is proposed. Selected bins to be encrypted are analyzed and tested to preserve format compliance and constant bit rate. As a result the decoder will not crash. Moreover, any standard decoder can be used without any modifications. This is of great importance to the encoding companies that use standard Codecs. The proposed approach is suitable and capable to be used in WMSN taking into account their limitations of resources. The obtained results confirm the high performance of the proposed approach with an overhead of less than 1%. The main contribution of this paper is the ability to use selective encryption based on tested and predefined bins that preserve format compliance and constant bit rate requirements during the compression cycle with minimum overhead. Thus, our proposed approach is applicable for real-time applications.
Light-weight residual learning for single image dehazing

Images captured on a hazy day are often degraded by the particle matter suspended in air, which introduces haze that severely distorts the quality of the acquired data. Eliminating haze, therefore, is a critical task in real-world applications. We propose a deep light-weight residual model, called image dehazing deep fully convolutional network (ID-DFCN), based on residual learning that directly projects the given hazy image to the residual onto the hazy image and the corresponding haze-free image. As a result, the haze-free image is obtained by adding the estimated residual. The proposed ID-DFCN is an end-to-end and light-weight model, which enables efficient hardware implementation. Qualitative and quantitative evaluations on synthetic and real-world hazy images show that the proposed model achieves comparable and even superior results in comparison to several state-of-the-art methods.
Telegram Bot Implementation in Academic Information Services with The Forward Chaining Method

Academic information is an important information for students in assisting academic activities, every effort has been developed to improve academic services. With the continuous development of various mobile devices or smartphones, the traditional way of Short Message Service (SMS) has been replaced by instant messaging applications that make the communication process more real-time. So this research will try to use technology in instant message as a means of academic service information, it is expected academic information can be delivered more quickly and up-to-date. Telegram as one of the instant messaging application that offers various advantages in its feature than other instant messaging application. The most popular feature and is being developed on telegram is the bot feature, where a third party or user can develop bot features according to user requirements. Thus telegram can help overcome various problems such as academic information seeking problems. For that made the application of lecture information service using Telegram Bot. The making of this information service application is built with Rational Unified Process (RUP) process model, Forward Chaining method and using Python Telepot Framework for Telegram Bot API for application to run via Telegram instant message. With the making of this application, facilitate communication and delivery of academic information to lecturers, students, and the academic community.
Architecture Modelling of Parametric Component-Based Systems

We study formal modelling of architectures applied on parametric component-based systems consisting of an unknown number of instances of each component. Architecture modelling is achieved by means of logics. We introduce an extended propositional interaction logic and investigate its first-order level which serves as a formal language for the interactions of parametric systems. Our logic effectively describes the execution order of interactions which is a main feature in several important architectures. We state the decidability of equivalence, satisfiability, and validity of first-order extended interaction logic formulas, and provide several examples of formulas describing well-known architectures.
ARMBIS: accurate and robust matching of brain image sequences from multiple modal imaging techniques

Study of brain images of rodent animals is the most straightforward way to understand brain functions and neural basis of physiological functions. An important step in brain image analysis is to precisely assign signal labels to specified brain regions through matching brain images to standardized brain reference atlases. However, no significant effort has been made to match different types of brain images to atlas images due to influence of artifact operation during slice preparation, relatively low resolution of images and large structural variations in individual brains.In this study, we develop a novel image sequence matching procedure, termed accurate and robust matching brain image sequences (ARMBIS), to match brain image sequences to established atlas image sequences. First, for a given query image sequence a scaling factor is estimated to match a reference image sequence by a curve fitting algorithm based on geometric features. Then, the texture features as well as the scale and rotation invariant shape features are extracted, and a dynamic programming-based procedure is designed to select optimal image subsequences. Finally, a hierarchical decision approach is employed to find the best matched subsequence using regional textures. Our simulation studies show that ARMBIS is effective and robust to image deformations such as linear or non-linear scaling, 2D or 3D rotations, tissue tear and tissue loss. We demonstrate the superior performance of ARMBIS on three types of brain images including magnetic resonance imaging, mCherry with 4',6-diamidino-2-phenylindole (DAPI) staining and green fluorescent protein without DAPI staining images.The R software package is freely available at https://www.synapse.org/#!Synapse:syn18638510/wiki/591054 for Not-For-Profit Institutions. If you are a For-Profit Institution, please contact the corresponding author.Supplementary data are available at Bioinformatics online.
KOMPARASI NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR UNTUK MENGETAHUI AKURASI TERTINGGI PADA PREDIKSI KELANCARAN PEMBAYARAN TV KABEL

One obstacle of the default payment is the lack of analysis in the new customer acceptance process which is only reviewed from the form provided at registration, as for the purpose of this study to find out the highest accuracy results from the comparison of Naïve Bayes, SVM and K-NN Algorithms. It can be seen that the Naïve Bayes algorithm which has the highest accuracy value is 96%, while the K-Neural Network algorithm has the highest accuracy at K = 3 which is 92%, while Support Vector Machine only gets accuracy of 66%. The ROC Curve results show that Naïve Bayes achieved the best AUC value of 0.99. Comparison between data mining classification algorithms namely Naïve Bayes, K-Neural Network and Support Vector Machine for predicting smooth payment using multivariate data types, Naïve Bayes method is an accurate algorithm and this method is also very dominant towards other methods. Based on Accuracy, AUC and T-tests this method falls into the best classification category.